Does Internal Control over Financial Reporting Curb Corporate

Does Internal Control over Financial Reporting Curb Corporate Corruption?
Evidence from China
Weili Ge
Michael G. Foster School of Business
University of Washington
Seattle, WA 98195
[email protected]
Zining Li
Cox School of Business
Southern Methodist University
Dallas, TX 75275
[email protected]
Qiliang Liu
School of Management
Huazhong University of Science and Technology
Wuhan, China, 430074
[email protected]
Sarah McVay
Michael G. Foster School of Business
University of Washington
Seattle, WA 98195
[email protected]
July 8, 2015

Ge and McVay would like to thank the Moss Adams Professorship and Glen & Lucille Legoe Professorship,
respectively, at the University of Washington for financial support. Liu acknowledges the financial support from the
National Natural Science Foundation of China (Project No. 71332008 and 71172206). Ge and Liu acknowledge the
financial support from the National Natural Science Foundation of China (Project No. 71328201 and 71332008). We
thank Charles Lee, Bret Johnson, Joe Piotroski, Holly Skaife, Brandon Szerwo, Siew Hong Teoh, Tianyu Zhang,
and workshop participants at Stanford University, the University of California at Davis, the City University of Hong
Kong, the University of Missouri at Columbia, University of Toronto, Huazhong University of Science and
Technology, Shanghai University of Finance and Economics, Southwestern University of Finance and Economics,
Sun Yat-sen University, Yunnan University of Finance and Economics, and Zhejiang University for helpful
comments. We thank the conference participants at the 2014 HKUST Accounting Research Symposium and the
2015 George Mason’s Investor Protection Research Conference for their helpful comments. We thank Professor
Hanwen Chen and his team for sharing the internal control index data developed under National Natural Science
Foundation of China Project No.71332008.
Does Internal Control over Financial Reporting Curb Corporate Corruption?
Evidence from China
Abstract
We examine whether internal control over financial reporting (internal controls) reduces the
extent that managers or controlling shareholders expropriate resources from the firm. Using a
sample of Chinese-listed firms, we find that after controlling for known determinants of
corruption and internal control effectiveness, managers and controlling shareholders at firms
with strong internal controls are significantly less likely to extract resources from the firm. We
find this relation is concentrated within non-state-owned firms, where internal controls are less
likely to be policy-driven, providing some evidence of the need to establish substance over form
when assessing the strength of internal control. Taken together, our results suggest that, on
average, strong internal controls curb corporate corruption; however, institutional factors and the
associated incentives play a significant role in the effectiveness of internal controls.
Keywords: Internal control over financial reporting; corporate corruption; state-owned
enterprises
I.
INTRODUCTION
We examine the relation between the strength of internal control over financial
reporting (hereafter internal control) and corporate corruption. 1 We define corporate
corruption as a form of corporate abuse in which controlling shareholders or managers
expropriate resources from the firm and therefore minority shareholders.2 For example, CEOs
are often persuaded through bribes to contract with inferior suppliers offering higher-priced
or lower-quality inventory relative to other suppliers. CEOs can also transfer funds to related
parties as loans with indefinite loan terms. Another common tactic for managers to extract
resources from the firm is through the reimbursement for their private consumption expenses.
Although corruption within Chinese firms has been well-documented (e.g., Jiang et al. 2010;
Cai et al. 2011; Piotroski and Wong 2012), our interest is in whether internal controls can
effectively combat this type of corruption. Similarly, although a number of studies we discuss
in the next section examine the impact of internal controls on financial reporting and real
activities, none have examined explicit evidence of corruption, or resource extraction; the
1
Internal control over financial reporting (ICFR) comprises the processes and procedures established by
management to maintain records that accurately reflect the firm’s transactions, and covers asset representation,
including asset misappropriation. Many of the same policies, procedures, and controls that lead to effective
ICFR therefore also affect firm resources and operations. We focus on ICFR rather than all internal controls for
several reasons. First, it is difficult to distinguish some broader internal controls from governance (e.g., an
independent board). Second, ICFR is the focus of Section 404 of the Sarbanes-Oxley Act and the subsequent
regulations within numerous countries, including China. Thus, our study provides evidence on whether these
ICFR-focused regulations can be effective in curbing corruption. In later analyses we partition ICFR by
operational controls and compliance controls, corroborating that it is operational ICFR that curb corruption.
2
We restrict our discussion of corruption to self-dealing by controlling shareholders or management extracting
resources from the firm at the expense of minority shareholders. This is related to corruption within the
government, given China’s unique institutional environment. Shleifer and Vishny (1993) define governmental
corruption as “the sale by government officials of government property for personal gain.” Over half of Chinese
publicly traded firms are state-owned firms (often termed as state-owned enterprises, SOEs), meaning that the
government is the controlling shareholder. As a result, the government appoints individuals who are often
current or former government bureaucrats as CEOs of state-owned enterprises.
1
closest provides indirect evidence of rent extraction via insider trading (e.g., Skaife et al.
2013).
China is well-suited as a setting to examine the effect of internal control strength on
corporate corruption. Unlike the U.S., within China, corruption is common and measurable.
In particular, because the legal system offers only weak protection for minority investors,
corporate abuse by controlling shareholders and managers is wide-spread and rather severe
(Jiang et al. 2010) and there is a high risk of controlling shareholders expropriating resources
from minority investors (Djankov et al. 2008). Common approaches of corruption
documented in prior research are the tunneling of cash from the firm through loans, which are
generally not repaid (Jiang et al. 2010), and the payment of private consumption expenses
with firm resources (e.g., extravagant dinners or gambling expenses; Cai et al. 2011). We also
consider ex post evidence of embezzlement or the receipt of bribes to enter into suboptimal
contracts.
Second, also distinct from the U.S., the required disclosures in China allow for a
continuous measure of internal control strength across all firms,3 whereas only a small subset
of firms with ineffective internal controls provide granularity on their underlying problems in
the U.S. 4 This allows us to distinguish between the design of internal controls, and the
implementation of internal controls. In particular, managerial override of existing internal
controls is deemed as “ineffective internal control” in the U.S., whereas in China, the ICFR
3
We use a proprietary database that tracks listed Chinese firms’ internal control information from financial
statements, filings to China Securities Regulatory Commission, government documents, and press releases
(Chen et al. 2013). The database covers 99 percent of all Chinese public firms from 2007 through 2010 and
allows us to measure the strength of internal control within the COSO framework.
4
For example, only 7.2% of firm-year observations disclose ineffective internal controls in the U.S. sample
examined in Feng et al. (2015). The other 92.8% simply disclose they maintain effective internal controls,
whereas our data exhibits variation in ICFR strength across the entire population.
2
score is the underlying existence of the internal controls, not whether these policies are
followed, which allows us to separate form versus substance.
We hypothesize that, to the extent that managers and controlling shareholders are
unable to completely override internal control mechanisms in place, stronger internal controls
will help mitigate the risk of corporate corruption as a stricter control and monitoring
environment would make it more difficult for top management or controlling shareholders to
engage in activities that are harmful to minority shareholders. For example, the creation and
implementation of a policy for how loans are determined and which expenses should be
reimbursed would reduce the likelihood of managers and controlling shareholders siphoning
cash from corporate accounts. Similarly, if a policy required three bids for each inventory
purchase, managers would be less able to receive bribes to contract with an inferior supplier.
If, however, managers are able to ignore or override the internal control policies and
procedures, the internal control system would not effectively deter corruption, and would
merely be window dressing (form over substance).
We study a sample of 4,775 firm-year observations from 2007 through 2010 of
Chinese firms listed on the Shanghai or Shenzhen Stock Exchanges. We use the data from
Chen et al. (2013) who collected 144 items related to various aspects of firms’ internal
controls, which we discuss extensively in Section III and Appendices A and B. We first pare
down the 144 items identified in Chen et al. (2013) to focus on internal controls over
financial reporting (64 items). We next provide evidence on the validity of our measure of
internal control strength by confirming that firms with higher internal control scores exhibit
less earnings management, measured by the absolute value of discretionary accruals and the
3
incidence of financial statement restatements. This finding is consistent with that of Chen et
al. (2013) using the broader measure and also with prior US-sample research examining
ICFR.
Following prior literature, we measure corporate corruption in the following ways: (1)
other receivables scaled by total assets and related party loans scaled by total assets to capture
the use of loans to tunnel funds out of the firm by controlling shareholders (Jiang et al. 2010),
(2) travel and entertainment costs scaled by sales to capture expenses incurred for managers’
private consumption (e.g., eating, drinking, sports club membership, and travel; Cai et al.
2011), and (3) public ex post disclosures of corruption by top management.5 We find that,
after controlling for known determinants of corruption and internal control strength, firms
with strong internal controls appear to tunnel fewer resources out of their firms using loans
and report lower travel and entertainment expenses, consistent with our prediction that
internal controls mitigate corporate corruption. We do not, however, find a significant
association between internal control strength and the frequency of disclosed corruption across
our full sample.
Next we examine whether the relation between internal control strength and corporate
corruption varies with whether the internal controls were more likely to be voluntarily
adopted or policy driven, proxied by state ownership structure (state-owned versus non-stateowned enterprises). 6 In 2006, in an effort to reform state-owned enterprises, the Chinese
5
Note that we remove the corruption cases in which managers bribe government officials to benefit the firm, for
example to protect against government expropriation or facilitate government services (e.g., to receive necessary
permits and licenses). We focus on corrupt behavior by management that is at the expense of minority
shareholders.
6
An important ownership characteristic of China’s listed firms is state ownership, where the government is the
controlling shareholder and appoints the top management team. State-owned enterprises (SOEs) comprise more
4
government issued numerous guidelines to underscore the importance of internal controls for
listed firms and applied a great deal of pressure on state-owned firms to follow the guidelines
in establishing specific internal control policies and procedures. Thus, although not mandated,
this pressure might have led managers of SOEs to implement boilerplate internal control
policies and procedures to satisfy government regulators, but without intent to enforce or
follow these internal control policies and procedures.7
This institutional difference might allow us to assess substance versus form, to the
extent that policy driven internal controls (those within state-owned enterprises) are less
effective at curbing corporate corruption compared to voluntary adoptions (those within nonstate-owned firms), either because the controls are designed, but not implemented, or because
managers simply override the controls. Consistent with at least some window dressing (i.e.,
form over substance), we find that, across all the corporate corruption measures, internal
control strength is less effective at curbing corporate corruption within state-owned firms
relative to non-state-owned firms.8
We conclude our analysis with several additional tests. First, we corroborate that our
measures of corruption are self-dealing and represent resource extraction from the firm.
than half of all firms listed on China’s stock exchanges (Piotroski et al. 2014). Managers of state-owned
enterprises tend to be former government officials who face multiple—and potentially conflicting—objectives
(e.g., political incentives versus incentives to maximize firm value). 7
Providing some evidence of this, we find that economic determinants of ICFR strength have lower explanatory
power for ICFR strength among SOEs relative to non-SOEs (Appendix D). We also find that among SOEs, the
establishment of strong internal control policies and procedures is positively associated with future CEO
promotions (Section V).
8
This finding also allows us to mitigate concerns that it is the crackdown on corporate corruption by the
Chinese government, rather than the internal controls per se, that have curbed corruption. If it were solely the
governmental oversight curbing corruption, we would expect it to either be pervasive across SOEs and nonSOEs, or be concentrated among SOEs, where the government has the greatest influence. Instead we find that
ICFR curbs corruption more among non-SOEs. We also include year fixed effects to further control for China’s
recent focus on curbing corruption. Finally, in our additional analyses we examine cross-sectional variation in
ownership structure within non-SOEs.
5
Consistent with this, we find evidence that corruption is negatively associated with future
operating performance. Second, we document cross-sectional variation within non-SOEs. In
particular, internal controls are more effective in curbing corrupt behavior when there is a
greater balance of power among the top shareholders, measured by when there are more
shares owned by the second to fifth largest shareholders. This finding is consistent with the
notion that within some firms (in our study either SOEs or non-SOEs that do not have a
balance of power among the top shareholders), internal controls may be easily over-ridden
and thus not effective, even if they are established. In contrast, in firms with a more balanced
ownership structure, internal controls are more effective. This could be because the board sets
more rigorous internal controls, and/or managers and controlling shareholders are less able to
override the controls. Third, we provide evidence that SOEs’ internal controls are influenced
by governmental pressure in that a) they appear to be less based on the underlying firm
economics, and b) managers implementing more internal controls in SOEs are more likely to
be promoted. We also consider a two-stage least-squares (2SLS) estimation procedure to
mitigate concerns of correlated omitted variables (such as management integrity) affecting
both the choice to establish internal controls and the choice to extract resources. We find
similar results when using the fitted value of ICFR strength.
Our findings make several contributions to the literature. First, we document the link
between internal controls and corporate corruption, which has not been documented to date.
Second, our results highlight that internal controls are significantly less effective in curbing
corporate corruption within state-owned enterprises. This suggests that institutional factors
and the associated incentives play a significant role in the effectiveness of internal controls
6
(similar to the findings of Lin et al. (2012) regarding independent director regulation). The
internal control disclosure requirements of the Sarbanes-Oxley Act in the U.S. have triggered
similar reforms in many other countries. However, our findings suggest that improvements in
internal controls might not have the intended benefits (e.g., curbing corporate abuse) across
all firms in emerging markets, especially when both ownership structure is highly
concentrated and protection for minority shareholders is lacking.
II.
BACKGROUND AND PREDICTIONS
Internal control over financial reporting (herein internal controls) is comprised of the
processes and procedures established by management to maintain records that accurately
reflect the firm’s transactions (Deloitte and Touche 2004). 9 Researchers have examined
various benefits of effective internal controls. Prior research has shown that higher internal
control strength reduces the unintentional errors in financial reporting (Ashbaugh-Skaife et al.
2008; Doyle et al. 2007), leading to more accurate management forecasts as managers use
more accurate financial inputs to form their forecasts (Feng et al. 2009), and improving
investment decisions (Cheng et al. 2013) as well as firm operating efficiency and firm
performance (Feng et al. 2015). These studies rely on the notion that the reports generated
from a system with ineffective internal controls contain errors (often unintentional errors),
and thus the information that management uses to create financial statements and make
decisions is faulty.
9
Following the Committee of Sponsoring Organizations of the Treadway Commission (COSO) framework
(2013), we define internal control as “a process, effected by an entity’s board of directors, management, and
other personnel, designed to provide reasonable assurance regarding the achievement of objectives relating to
operations, reporting, and compliance.” The focus of this paper is on internal control over financial reporting,
but in our robustness checks, we also provide evidence on internal controls that are not related to financial
reporting.
7
There is limited evidence, however, on whether internal controls reduce managers’
rent extracting behavior. Donelson et al. (2014) examine the association between internal
control weaknesses and intentional financial distortion (fraud), but do not examine resource
extraction. Skaife et al. (2013) provide some evidence of rent extraction in that the
profitability of insider trading is greater in firms with ineffective controls. They suggest that a
weak internal control environment provides managers with an information advantage,
enabling them to profit from their private information by selling before stock price declines.
They do not provide evidence, however, of the more direct resource extraction we consider.
In particular, although selling personal shares at a profit is evidence of opportunism, it is both
more indirect and less costly to shareholders than the more egregious and direct form of
corruption we examine.10 This is especially salient given Skaife et al. (2013) find only limited
evidence of managers managing earnings before selling their personal shares. The focus of
our study is whether internal controls reduce the extent of corporate corruption, which is an
escalated form of managerial rent extracting behavior that represents a much more direct and
quantifiable cost to shareholders.
The limited research on internal controls and resource extraction in the U.S. is not
surprising. Strong and well-enforced investor protection in the U.S. constrains insiders’
ability to acquire private control benefits (Leuz et al. 2003; La Porta et al. 2000); as a result,
U.S. firms exhibit significantly less corporate corruption, on average, than firms in countries
10
As an example, the CFO of South Airline Co., Liming Chen, tunneled cash from the company to related
parties using numerous loans. For instance, he took a corporate loan of 30 million Chinese Yuan from China
CITIC Bank, and then moved the money in the form of other receivables to a company controlled by his friend,
Zhuangwen Yao. To return the favor, Zhuangwen Yao, gave Chen a BMW worth 700,000 Chinese Yuan and a
house worth 2.25 million Chinese Yuan as gifts. In addition, Chen took bribes of over 53 million Chinese Yuan
from various sources to enter into various contracts.
8
with poor investor protection. It follows that there is no strong pattern of, or evidence on,
how insiders engage in corporate corruption in the U.S., making it difficult to measure
corruption.
Using a sample of Chinese firms allows us to overcome these limitations. China has
particularly weak minority investor protection, in part because China’s legal system lacks
enforcement.11 Allen et al. (2005) provide evidence that among seven developing countries,
China’s corruption index is ranked as the most severe. 12 Thus, corrupt behavior by
controlling shareholders and managers is wide-spread (Jiang et al. 2010). This corruption is
also measurable. Prior research provides evidence on common approaches through which
controlling shareholders or managers expropriate resources from minority shareholders in
China (Jiang et al. 2010; Cai et al. 2011). Therefore, we are able to measure specific
corporate corruption such as the tunneling of cash from the firm through loans, the payment
of lavish entertainment expenses and other private consumption expenses with firm resources,
or the ex post revelation that managers accepted bribes (for example to contract with inferior
suppliers) or embezzled from the firm.
We predict that it is more difficult for managers and controlling shareholders to
extract resources from the firm in a stricter internal control environment. According to the
government guidelines, the Board of Directors is in charge of establishing internal control
systems. Therefore, to the extent that managers and controlling shareholders are unable to
11
According to the 2014 report by Word Bank Group, China was ranked 132 out of 189 countries in terms of
the strength of minority shareholder protections.
12
These seven countries are: China, India, Pakistan, South Africa, Argentina, Brazil, and Mexico. Allen et al.
(2005) base their inferences on the International Country Risk Guide’s assessment of the corruption in
government. Lower scores suggest that “high government officials are likely to demand special payments” and
“illegal payments are generally expected throughout lower levels of government” in the form of “bribes
connected with import and export licenses, tax assessment, policy protection, etc.”
9
completely override internal control mechanisms in place, we expect internal controls to
reduce the extent of their rent extraction behavior. For example, the common technique of
issuing loans to transfer cash (i.e., tunneling) would be curbed by the requirement of a loan
approval process that spells out interest and repayment terms. Related controls would trigger
personnel to follow up on expected interest/principal payments that have not been received.
With respect to private consumption by management, it is possible that requiring separate
personnel to approve versus pay for invoices (i.e., segregation of duties) would curb much of
the inappropriate reimbursement of private consumption expenses. In addition, beyond the
establishment of segregation of duties, routine reviews of expense reports or maintaining a
clear reimbursement policy would likely further reduce corrupt behavior of charging for
items used for personal reasons or simply faking receipts. As another example related to
accepting bribes, stricter purchase order authorization would ensure that managers are not
over-ordering or ordering at an unreasonable price. We state our first hypothesis as the
following, in the alternative form.
H1: Strong internal controls reduce corporate corruption (resource extraction from the firm).
Even in the presence of internal controls, managers or controlling shareholders may
simply fail to implement or enforce these controls, and may also override the controls. Thus,
if we do not find evidence consistent with H1, it could be because internal controls do not
curb corruption, but it could also be due to the design of internal controls that are not
effective (i.e., form over substance). Our next hypothesis should help provide evidence on the
underlying reason for insignificant results.
10
Our second hypothesis examines whether the relation between internal control
strength and corporate corruption varies with whether the internal controls were more likely
to be voluntarily adopted or policy driven. To proxy for the construct of policy driven, we
examine the ownership structure of the firm. Specifically, we expect that state owned firms in
China receive more pressure to improve internal controls, and thus these controls are more
likely to be policy driven than voluntary, which may influence the effectiveness of internal
control in curbing corporate corruption.
As of 2010, sixty-five percent of listed firms in China were state-owned enterprises,
accounting for 89 percent of total market capitalization in China (Piotroski et al. 2015). The
government is the controlling shareholder of state-owned enterprises and appoints key
executives such as the CEO and the Chairman of the board. As a result, the top managers of
state-owned enterprises have multiple objectives. In addition to profit maximization, they
might also aspire to improve employment rates or build relationships with government
superiors; these other objectives could cause significant inefficiencies for the firm (see
Piotroski and Wong 2012; Piotroski et al. 2015).
Following the implementation of the Sarbanes-Oxley Act in the U.S., the Chinese
government began to emphasize improving publicly listed firms’ internal controls. In June
2005, the Ministry of Finance, the China Securities Regulatory Commission (CSRC), and the
State-Owned Assets Supervision and Administration Commission (SOASAC) jointly issued
the “Report on Learning from Sarbanes Oxley to Strengthen Our Listed Firms’ Internal
Controls”. On March 5, 2006, Premier Jiabao Wen emphasized during the Fourth Plenary
Meeting of the Tenth People’s Congress that “we need to introduce and learn from other
11
countries’ experiences in corporate governance, standardize governance mechanisms, and
improve internal control systems.” A series of governmental guidelines were issued following
Premier Wen’s address. The central government wanted to establish “role models” of stateowned enterprises to benefit the social goals of the government. The guidelines issued by
SOASAC state that central-government-controlled enterprises should develop internal control
systems and prevent corruption. Moreover, the “Basic Standards for Large and Median SOEs
on Developing Modern Enterprises System and Strengthening Corporate Governance” issued
by the General Office of the State Council of PRC states the following:
“Those state-owned enterprises classified as major enterprises by the central
and local governments, are required to identify deficiencies according to the
Standards and make improvements to comply with the Standards. All other
enterprises should also follow the Standards and strive to meet all the
requirements. The committee on trade and economy, and the budget
committee at the central government and local governments are called to
conduct research and investigation on the effectiveness in implementing the
standards.”
Clearly, state-owned enterprises are under pressure from the government to establish certain
types of internal control procedures, which is compounded by managers of the state-owned
enterprises (current or former government bureaucrats) having incentives to maintain strong
political connections with government officials.
It is possible that state-owned firms may adopt a boilerplate list of internal control
procedures to satisfy government regulators but do not actually implement or enforce these
internal control policies and procedures. As a result, such policy-driven burdens (e.g., internal
controls) may not be effective in achieving the stated goal of reducing corporate corruption.
As suggested in Lin et al. (1998), policy burdens reduce the efficiency of state-owned firms’
operations. In addition, as argued in Piotroski and Wong (2012), greater state involvement in
12
an economy creates incentives for financial reporting opacity to hide the rent-seeking
activities of politicians and related parties.
In sum, although management of state-owned enterprises have incentives to adopt
strong internal controls on paper, they likely have fewer incentives to actually realize the
benefits of these internal control practices, relative to firms that voluntarily choose to
establish strong controls. Because they have the power to override or simply ignore internal
controls in place, the most corrupt managers are unlikely to be restrained by internal controls.
As an example, Zhaolu Zhou, the CEO of Yunnan Copper Co., a state-controlled enterprise
with a market capitalization of 14 billion Chinese Yuan, received at least 19 million Chinese
Yuan in bribes to award certain contracts at the expense of shareholders, and did so by
overriding internal controls.13
We thus hypothesize that policy-driven adoptions of internal control guidelines and
procedures are less effective in reducing corporate corruption:
H2: Strong internal controls are less effective at curbing corporate corruption when they are
policy driven relative to when they are voluntarily adopted.
We use the adoption of internal controls within state-owned enterprises to proxy for policydriven adoptions and conduct several tests to assess the validity of this proxy.
III.
DATA, SAMPLE, AND CORPORATE CORRUPTION MEASURES
Internal Control Index and Sample Selection
Our ICFR measure is based on the underlying data used in Chen et al. (2013). These
data cover 99% of all Chinese listed firms from 2007 to 2010 and indicate whether 144
13
Specifically, Zhao acknowledged in his self-reflection report that he had too much power and was able to
override the internal control systems within the firm. Self-reflection reports are a requirement of bureaucratic
prisoners in China, who are required to detail their transgressions and the motivations for their actions.
13
specific firm features exist within each firm-year. These 144 firm features each fall within the
five main aspects of internal control proposed by COSO: (1) Control Environment, (2) Risk
Assessment, (3) Control Activities, (4) Information and Communication, and (5) Monitoring
(see Appendix A).14 As our focus is ICFR, we focus on the existence of 64 of the 144 firm
features collected for the initial index (see Appendix B). Each of the 64 items receives a
value between zero and one, which we then average within each control aspect (three-digit
level in Chen et al. 2013). 15 Finally we aggregate the values from each control aspect and
calculate an average ICFR score ranging from zero to one. See Appendix B for a more
detailed description of the calculation of our ICFR score. Appendix C provides the definitions
for all of our variables.
We present the sample selection procedure in Table 1, Panel A. Our sample begins
with all Chinese firms listed on the Shanghai and Shenzhen stock exchanges with internal
control index data from 2007 through 2010. We remove 120 firm-years in the financial
industry as financial firms are under different internal control requirements issued by the
People’s Bank of China and the China Securities Regulatory Commission. We then delete
14
These data were collected by the research team led by Hanwen Chen, supported by China NSF grant
#71332008, and Ministry of Education Social Science Major Research grant #10JJD630003. Since its creation,
the index has gained recognition from researchers, practitioners and regulators as the metric of Chinese firms’
internal control effectiveness. These data are considered the most comprehensive and authoritative detail on
internal control by Chinese regulators and security market participants. For example, on June 11, 2010, all three
of the most authoritative Chinese financial newspapers, the China Securities Journal, Shanghai Securities News,
and Securities Times, featured articles introducing the index. The researchers who developed the internal control
index annually publish the top 100 firms that have the highest internal control scores in the China Securities
Journal. Deloitte highlighted this index on the website of its Corporate Governance Center; and the Public
Company Monitoring Division of the Shanghai Stock Exchange also acknowledged that the index “has
significant reference values for our efforts of monitoring internal control and corporate governance.”
15
Sixty-two of the 64 items receive a score of one if a certain feature is in existence and zero otherwise; the two
remaining items receive a standardized score ranging from zero to one. Of the 64, 10 relate more to compliance
than to operational controls; in our robustness checks we partition the score further into ICFR_OPERATIONS
and ICFR_COMPLIANCE and find our results are concentrated within ICFR_OPERATIONS. We discuss this
analysis in Section V.
14
1,401 firm-year observations that are traded on the small and medium-sized enterprises
(SMEs) board because these firms are subject to different internal control and disclosure
requirements (e.g., they are only required to provide internal control reports every other year).
Of the remaining firms, 468 firm-years do not have necessary data for the analysis on the
determinants of internal control, resulting in a sample of 4,775 firm-year observations. We
obtain the information on firms’ stock prices, company financials, industry classification,
ownership structure, auditors, and largest shareholders from the Chinese Stock Market and
Accounting Research (CSMAR) database. As shown in Panel B of Table 1, our sample is
evenly distributed across our sample period, and about 67% of our sample firms are stateowned enterprises, i.e., the controlling shareholders are either the central or local government,
or their agencies.
[Table 1]
We validate our ICFR Score in two ways. We first explore the underlying
determinants of our ICFR Score confirming that it exhibits expected associations with
previously examined determinants of ICFR strength. We also confirm the association
between ICFR and financial reporting quality documented in prior research (e.g., Doyle et al.
2007b; Chen et al. 2013) to further validate our internal control measure. Appendix D
provides a detailed description of these validity tests. These associations help to mitigate
concerns about measurement error in our proxy of ICFR strength. In particular, although
some assessments require subjectivity, and others could reflect window dressing, both of
these measurement errors should add noise and thus lower the power of our tests. That we
find the expected associations with both determinants and known consequences of ICFR
strength mitigates this measurement error concern.
15
Measures of Corporate Corruption
The first form of corporate corruption that we analyze is tunneling through the use of
loans with indefinite repayment terms (hereafter tunneling). Jiang et al. (2010) document that
tunneling is a prevalent and persistent method used by controlling shareholders to expropriate
funds from minority shareholders. 16 We use two measures to capture tunneling behavior.
Following Jiang et al., we first use other receivables scaled by total assets (OTHREC). In
addition, Chinese listed firms are required to disclose related party loans in their annual
reports; thus we adopt a second measure (RELATEDLOAN) that is the amount of loans from
the listed firm to disclosed related parties (including the controlling shareholder, other
companies controlled by the same controlling shareholder, subsidiaries, and joint ventures)
scaled by year-end total assets. We view OTHREC and RELATEDLOAN as complementary.
OTHREC could capture the expropriation of funds via loans that are not disclosed as related
party loans in annual report (e.g., funds to unidentified related parties) while
RELATEDLOAN specifically captures lending behavior to related parties, but could miss
other tunneling. At times, both measures will capture the same loan; the Spearman correlation
between OTHREC and RELATEDLOAN is 0.36.
Tunneling captures one specific approach through which controlling shareholders or
managers can expropriate funds for personal gain. Prior research has also shown that
corporate executives in China often misuse corporate funds for their private consumption
such as dining, travel and entertainment. In China, entertainment providers generally are
willing to provide generic or modified receipts for their clients, in order to facilitate
16
Clearly some of these loans are valid, with reasonable interest rates and standard repayment terms. These
valid loans should serve to add noise, but not bias, to our analysis. We expect that the pervasiveness of tunneling
has declined since the Jiang et al. study, but continue to find evidence of tunneling (see also footnote 21). We
corroborate our inferences in Section V by documenting that this tunneling behavior is associated with poorer
future performance.
16
reimbursement by their employers. Thus extremely lax accounting regulations and
enforcement allow the reimbursement of private consumption expenses to be classified as
business related administrative expenses. 17 We manually collect the information from the
notes to financial statements in the annual reports on the following expenditures: office
supplies, business travel, entertainment, communication, training abroad, board meetings,
automobile, conferences, and other expenses. Thus, our second proxy of corruption is the
sum of these expenditures scaled by sales (PRIVATE) which is intended to capture managers’
expropriation of corporate funds for private consumption (Cai et al. 2011).18
Our final measure of corruption is the ex post detection and disclosure of corruption
by either regulators or the media. We collect exposed corruption cases from both main stream
Chinese media sources and litigation cases to ensure the most comprehensive coverage.19 We
require that this disclosure indicates self-serving behavior (e.g., embezzlement or the receipt
of bribes) by management. We analyze each corruption case to determine the timing of
corrupt behavior (i.e., when management undertakes the corrupt behavior), and set
EXPOSED equal to one for the firm-years with the corrupt behavior, and zero otherwise.20
17
Anecdotal support of this behavior is common. As covered in Wall Street Journal on November 4th, 2014, the
casinos in Macau (a Chinese territory) experienced their largest sales decline ever of 23% in October of 2014.
“Industry executives and analysts attribute the recent poor performance in Macau to a variety of factors,
particularly a Beijing-led crackdown on corruption that has caused VIP gamblers to shy away from the baccarat
tables,” (O’Keeffe 2014).
18
PRIVATE could also capture the expenditures used to bribe external parties to negotiate deals that benefit the
firm. It is likely that internal controls will not curb such bribing behavior as it benefits the firm; therefore, this
might weaken the effect of internal control on PRIVATE.
19
To collect publicly disclosed corruption cases, we first search the “China Economic News Database,” which
consists of articles published in all Chinese newspapers and periodicals. We use the following keywords (in
Chinese) to conduct the news search: corruption, Shuanggui (detained and interrogated), stepping down,
economic issues, embezzlement, misappropriation, bribery, job suspension, favoritism, property transfer, and
Xiake (a synonym of stepping down). Next we searched within all legal case documents that are relevant to
corruption from the courts in accordance with “LawInfoChina” (LawInfoChina is a data center for court legal
documents). Finally, we used the Baidu search engine to retrieve public companies’ corruption materials based
on company names identified from our search.
20
We do not include corruption cases that involve giving bribes to external parties (e.g., government) for the
firm’s benefits (e.g., obtaining a contract with the government). Of the 159 cases involving exposed corruption,
120 cases relate to accepting bribes at the expense of shareholders (e.g., to buy inventory at above-market
prices). The remaining cases involve embezzlement. Because some corruption cases involve multiple years, we
have 214 firm-year observations with EXPOSED = 1. To minimize the effect of Type II errors on our analyses
17
We examine the association between internal control and corrupt behavior at the time the
corruption occurred (not at the time of ex post disclosure). One advantage of this measure
(EXPOSED) is that we have a high level of confidence regarding the existence of corrupt
behavior (the type I error rate is low). These detected and exposed corruption cases tend to be
egregious in nature. One disadvantage, however, is that many corruption cases likely remain
unidentified (the type II error rate is high) and there could be selection biases in the exposed
cases. In contrast, it is likely that our other measures (OTHREC, RELATEDLOAN and
PRIVATE) have higher type I error rates, but lower type II error rates. We also note that
tunneling behavior (OTHREC and RELATEDLOAN) is more likely to capture corrupt
behavior by controlling shareholders while private consumption (PRIVATE) and accepting
bribes (EXPOSED) is more likely to capture corrupt behavior by management. Thus, it is
important to consider evidence from all of our corruption measures. To corroborate our main
analysis, in Section V we provide evidence on the ex post performance consequence of
corporate corruption; that is, whether greater corporate corruption is associated with lower
future firm performance.
We report descriptive statistics of the main variables in Panel A of Table 2. In our
sample, the mean (median) of ICFR Score is 0.465 (0.460). This value is relatively sticky
across time for the same firm, with an autocorrelation of 0.679. The mean (median) of
OTHREC (tunneling) is 2.7% (1.2%) of total assets, where the mean (median) of total assets
is 6,966 (2,504) million Chinese Yuan.21 The mean (median) of RELATEDLOAN is 1.2%
(i.e., undetected corruption cases contaminating our control sample), we remove firms with other types of CSRC
regulation violations or qualified audit opinions from the control sample.
21
The pervasiveness of tunneling was greater prior to the “crackdown” on these loans by the China Securities
Regulatory Commission following the public disclosure of the evidence in Jiang et al. (2010). Thus, the mean of
OTHREC in our sample is lower than that of Jiang et al. (2.7% versus 8.1%). Nonetheless, the extraction of
resources via the tunneling of 2.7% of total assets remains material. To put this number in perspective, the mean
of ROA is 3.6%. Further, we conducted a falsification test to examine whether ICFR influences accounts
receivable the same way as other receivables. Unlike OTHREC, we do not find a negative association between
18
(0.0%) of total assets. The mean (median) of PRIVATE is 2.7% (1.0%), where the mean
(median) of sales revenue is 4,935 (1,444) million Chinese Yuan. During our sample period,
on average 4.5% of sample firms are detected and exposed for self-dealing corruption such as
accepting bribes or embezzling assets.
[Table 2]
We report descriptive statistics by state-owned enterprises (SOEs) and non-SOEs in
Panel B of Table 2. Consistent with the push by the government to establish internal control
procedures within SOEs, the ICFR scores are higher for SOEs than non-SOEs at both the
mean and median. For example, the mean score for SOEs is 0.475 versus 0.446 for non-SOEs.
In Appendix D we explore the determinants of ICFR Score and see that SOEs continue to
have higher ICFR after controlling for standard determinants of internal control strength,
such as size and complexity.
With respect to our corruption measures, tunneling (OTHREC and RELATEDLOAN)
and reimbursement of private consumption expenses (PRIVATE) appear more pervasive
among non-SOEs whereas there are notably more instances of publicly disclosed corruption
within SOEs, with the mean value of EXPOSED equal to 0.062 for SOEs compared to a mean
value of 0.011 for non-SOEs. Thus, there is some evidence that managers of non-SOEs are
more likely to extract funds via loans that they do not expect to repay or through questionable
expense reimbursements relative to managers of SOEs, who are more likely to accept bribes
to enter suboptimal contracts, or embezzle from their firms.
ICFR and accounts receivable. This result is consistent with the notion that OTHREC likely captures tunneling
behavior that is beyond legitimate operating activities. We provide evidence in Section V on the potential
negative consequence of tunneling during our sample period by examining how tunneling influences firms’
future performance to corroborate our inferences that OTHREC is costly to the firm.
19
SOEs are generally larger than non-SOEs, but are slightly less profitable, consistent
with these firms and managers having objectives other than solely profit maximization.
Finally, the Big Four audit firms have a much larger presence within SOEs, although the vast
majority of firms are audited by smaller audit firms, consistent with prior research (e.g., Chen
et al. 2011). Panel C of Table 3 presents the descriptive statistics of ICFR Score and our
corruption measures by industry. It appears that the construction industry has the highest
average ICFR Score (0.516) while the industry of agriculture, forestry and fishing has the
lowest average ICFR Score (0.427). Generally it appears that although our corruption
measures are populated across industries, our tunneling measures are most prevalent in
Agriculture, Construction and Information Technology, PRIVATE is highest within
Information Technology and Media (the latter perhaps reflecting a necessary cost in that
industry) and EXPOSED is highest in Mining, Utilities, and Transportation. Panel D of Table
2 presents the means of ICFR Score and our corruption measures over time. In general,
internal control strength has improved over time. Our corruption measures show a similar
decline across time, consistent with improvements in ICFR curbing corruption. Turning to the
correlation table in Panel E of Table 3, we find that ICFR Score is negatively correlated with
OTHREC, RELATEDLOAN, and PRIVATE (consistent with H1), but not EXPOSED. From
the low correlations of our corruption variables (except for the previously noted correlation
between OTHREC and RELATEDLOAN), it is clear that each captures a distinct component
of corruption.
IV.
INTERNAL CONTROL AND CORPORATE CORRUPTION
Internal Control and Tunneling
To explore whether internal controls curb tunneling, we estimate the following model:
20
OTHREC or RELATEDLOAN = β0 +β1ICFR +β2ASSET +β3ROA +β4TOP1SHR
+β5INVCOMP +β6BOARD +β7CEOCHAIR + β8MARKETIZATION +β9BIG4
+β10EXCHANGE + ε
(1)
The dependent variable is either OTHREC or RELATEDLOAN, both of which are intended to
identify the existence of tunneling (cash loans that are not expected to be repaid). OTHREC is
other receivables scaled by year-end total assets and RELATEDLOAN is the amount of loans
from the listed firm to related parties scaled by year-end total assets. ICFR is a variable
ranging from zero to one with a higher value indicating stronger internal controls over
financial reporting (see Appendix B). H1 predicts that effective internal controls reduce the
extent of corporate corruption; therefore, we expect the coefficient on ICFR to be negative.
Following Jiang et al. (2010), we include firm size (ASSET), profitability (ROA), shares
owned by the largest blockholder (TOP1SHR), and the development of the regional market in
which the firm is registered (MARKETIZATION) as control variables. Jiang et al. (2010)
show that tunneling decreases in firm size and firm profitability. They also show that
tunneling decreases in the ownership of the controlling shareholder because a large
ownership interest mitigates the controlling shareholder’s incentives to extract private
benefits that would destroy firm value. Tunneling is also expected to be more serious in a less
developed regional market as less developed local markets are associated with weaker legal
and regulatory environments. In addition to the controls included in Jiang et al. (2010), we
include CEOCHAIR which equals one if the CEO is also the Chairman of the board, because
we expect more powerful CEOs to have more opportunities to misappropriate corporate funds.
We also include two control variables based on corporate governance items collected by
Chen et al. (2013). Specifically, INVCOMP is a summary measure based on the four items
related the shareholder composition (e.g., percentage of ownership by institutional investors)
and BOARD is a summary measured based on the five items related to the characteristics of
21
the board of directors (e.g., the number of board committees). 22 These two variables are
described in detail in Appendix C. We include BIG4 to control for differences in audit quality
and EXCHANGE to control for different regulations and requirements under the two
exchanges, although we do not have a directional prediction for this variable. Finally we
include year and industry fixed effects in the regression.
We report the regression results in Table 3 Panel A. Consistent with H1, the
coefficient on ICFR is significantly negatively associated with both tunneling measures (e.g.,
coefficient = –0.013; p-value = 0.048 for OTHREC), suggesting that stronger internal
controls curb tunneling. The coefficients on the control variables are consistent with those in
Jiang et al. (2010). Specifically, tunneling is decreasing in firm size (ASSETS), profitability
(ROA), percentage of shares owned by the largest shareholder (TOP1SHR), and the degree of
development of regional markets (MARKETIZATION). We also find that higher institutional
ownership and more active investors (INVCOMP) are associated with less tunneling behavior.
We do not find a significant association between tunneling and the CEO also being the
Chairman, nor between tunneling and audits by the Big Four.
Next we examine H2 by estimating Equation (1) separately for SOEs and non-SOEs.
The regression results are presented in Table 3, Panel B. Interestingly, for both tunneling
measures, ICFR is no longer significant among SOEs (e.g., coefficient = –0.006; p-value =
0.207 for OTHREC), but is a strong predictor of tunneling within non-SOEs (e.g., coefficient
= –0.032; p-value = 0.025 for OTHREC). The coefficient on ICFR is statistically different
22
This variable includes the number of dissenting proposals by independent directors, which offers a stronger
measure of governance than independent directors in China, who may be ineffective (Lin et al. 2012).
22
between SOEs and non-SOEs (e.g., p-value = 0.068 for OTHREC).23 This result suggests that
although internal control over financial reporting effectively mitigates tunneling within nonSOEs, this is not the case within SOEs. This result is consistent with our prediction that
policy-driven adoptions of internal control are not as effective as adoptions that are more
voluntary in nature (H2), highlighting the importance of substance over form.
In summary, the results based on tunneling as a form of corporate corruption are
consistent with both H1 and H2. We find a negative association between internal control
strength and the extent of controlling shareholders harming minority shareholders by
extracting resources from the firm via tunneling. Within SOEs, however, internal controls are
not associated with this corrupt behavior.24
[Table 3]
Internal Control and Private Consumption
The preceding analysis provides evidence on the effect of internal control on
corporate corruption specific to tunneling. Corporate corruption, however, manifests in
various ways. As discussed in Section III, our next measure of corporate corruption is private
consumption expense reimbursement (PRIVATE). Thus, we estimate the following model,
where the dependent variable is PRIVATE.
23
To test whether the main coefficients are the same across different SOE types, we use the following Z-
statistics: ܼ ൌ
௕೔ ି௕ೕ
ට௦మ ሺ௕೔ ሻା௦మ ሺ௕ೕ ሻ
;
where bi and bj are coefficient estimates from the two sub-samples, and s2(b) is the squared standard errors of
the coefficients (Clogg et al. 1995, Chen et al. 2010).
24
This could reflect that ICFR does not constrain tunneling, but could also reflect that tunneling is not present in
SOEs. To explore which of these is more descriptive, we consider the “costs” to tunneling on future operating
income and find that tunneling is associated with lower future operating income among SOEs, providing some
evidence that tunneling is present among SOEs (see Section V).
23
PRIVATE= β0 +β1ICFR +β2PAYRATIO +β3MGMTSHR +β4ATO +β5ROA + β6LOSS
+β7EISSUE +β8DISSUE +β9BIG4 +β10TOP1SHR +β11INVCOMP +β12BOARD
+β13CEOCHAIR +β14MARKETIZATION
(2)
PRIVATE is the sum of expenditures for office supplies, business travel, entertainment,
communication, training abroad, board meetings, automobile, conferences, and other
expenses scaled by sales. We predict a negative coefficient on ICFR because stronger internal
controls should limit the magnitude of private benefits resulting from the corruption. We
include several control variables from prior research. First, executives with lower pay are
more likely to misappropriate corporate funds and assets for private consumption. Chen et al.
(2005) find that executives substitute lower pay with excessive perquisite consumptions. We
follow their measure of executive compensation and control for the ratio of executive total
compensation relative to other employees’ average compensation (PAYRATIO). We also
control for management ownership (MGMTSHR), measured as outstanding shares owned by
executives, because management ownership helps align managers’ objectives with
shareholders’ interests and reduces incentives for excessive perquisite consumption. We next
include asset turnover (ATO) as a control variable to control for the effect of operating
efficiency on selling, general and administrative expenses. We control for firm performance
by return on assets (ROA) and whether there is a loss (LOSS) but we do not have a clear
prediction on these two variables. On one hand, more profits provide more resources for
corrupt executives; on the other hand, uncorrupt managers might generate better performance
(e.g., they will buy the best-value inventory). We control for new issuances of equity
(EISSUE) and debt (DISSUE) since the expenditures included in our PRIVATE measure may
24
increase during financing activities. Lastly we control for BIG4, TOP1SHR, INVCOMP,
BOARD, CEOCHAIR, and MARKETIZATION as in the tunneling analysis.
Table 4 presents the regression results for PRIVATE, our proxy for private
consumption expense reimbursement. We find a significant negative association between
ICFR and PRIVATE for the full sample (coefficient = –0.025; p-value = 0.047), suggesting
ICFR effectively reduces corruption related to private consumption, again supporting H1. We
again find that ICFR is insignificant within SOEs (coefficient = –0.002; p-value = 0.459), but
is a strong predictor of corruption within non-SOEs (coefficient = –0.069; p-value = 0.027).
The coefficient on ICFR within non-SOEs is statistically and economically larger in
magnitude than the coefficient on ICFR within SOEs, in support of H2.25
[Table 4]
Internal Control and Exposure of Corrupt Behavior
Our final measure of corporate corruption is disclosed corruption cases (EXPOSED).
We estimate the following model, where the dependent variable is EXPOSED:
EXPOSED= β0 +β1ICFR +β2PAYRATIO +β3MGMTSHR +β4SALES +β5ROA
+β6EISSUE +β7DISSUE +β8TOP1SHR +β9INVCOMP +β10BOARD
+β11CEOCHAIR +β12MARKETIZATION + ε
(3)
EXPOSED equal to one if we identify an exposed case of corrupt self-dealing (e.g.,
embezzlement or the receipt of bribes to conduct suboptimal transactions) from either main
stream Chinese media sources or litigation cases. In addition to the corruption determinants
included previously, we also include SALES as a control variable because a majority of the
exposed cases (120 out of 159 cases) relate to accepting bribes (e.g., to buy inventory at
25
We also examine the robustness of this result by adopting a two-stage approach. First, we model PRIVATE by
regressing PRIVATE on the potentially legitimate determinants (i.e., SOE, firm age, growth, leverage, size,
ROA, the number of employees, and year and industry fixed effects) and then we estimate Equation (2) by using
the residuals from the first stage regression. The results are inferentially similar (not tabulated).
25
above-market prices). CEOs of firms with more sales might have more opportunities to
conduct this type of corruption (e.g., more suppliers will vie for the opportunity to bribe these
managers as the gain is larger). We predict a negative coefficient on ICFR if stronger internal
controls limit the opportunity for corporate corruption.
We provide the regression results for Equation (3) in Table 5. The association
between ICFR and EXPOSED is insignificant for the full sample and the SOE sample, and is
marginally significantly negative for the non-SOE sample. This is generally consistent with
our results on tunneling and private consumption in that internal controls appear to be
effective at reducing corruption within firms owned and controlled by entrepreneurs and
private investors, but not within state-owned enterprises. 26 We find a significant negative
association for the level of executive pay relative to other employees (PAYRATIO) within the
full, SOE and non-SOE samples, suggesting that a lower relative executive pay creates
incentives for managers to accept bribes or embezzlement. Management ownership seems to
reduce the incentives to engage in corruptions as results indicate a positive association
between EXPOSED and MGMTSHR for the full and non-SOE samples. We also find some
evidence that powerful CEOs (CEOCHAIR = 1) are more likely to accept bribes or embezzle
within non-SOEs.
[Table 5]
Taken together, our results from Tables 3–5 provide evidence that the strength of
internal controls over financial reporting curbs corporate corruption (H1). Internal controls
26
Exposed bribery cases are concentrated in SOEs, which is consistent with the larger size and greater scrutiny
of SOEs. This bias should serve to work against us finding an association between ICFR and EXPOSED among
non-SOEs.
26
within state-owned enterprises, however, are much less effective at curbing corruption across
all four of our tests, consistent with the importance of substance over form (H2).
V.
ADDITIONAL ANALYSES AND ROBUSTNESS TESTS
Corroboration of Corruption Measures
An underlying assumption in our prior tests is that our measures of corruption capture
expropriation of resources from the firm and minority shareholders. If such corrupt behavior
truly harms the firm, then we expect to observe worse ex post firm performance for firms
exhibiting more corrupt behavior. Thus, we expect our measures of corruption to be
associated with lower future firm performance. We estimate the following model, where the
dependent variable is one-year-ahead operating income scaled by average total assets (OIt+1):
OIt+1= β0 +β1Corruption measures +β2PPE +β3 GROWTH +β4SEGMENTS +β5EXPORT
(4)
+β6AGE +β7BIG4 +β8OI +β9ASSETS +β10TOP1SHR + ε
We base our control variables on those considered by Feng et al. (2015), who examine
ex post performance following ineffective internal controls (see their Table 8), and include
TOP1SHR following Fan et al. (2007). The results are reported in Table 6. We find that
OTHREC, RELATEDLOAN, and EXPOSED all have a significantly negative association with
one-year-ahead operating income. These results suggest that our corruption measures do
capture management behavior that is detrimental to subsequent firm performance. PRIVATE,
however, is not significant in predicting one-year-ahead operating income. Among the
control variables, we find that future operating income is higher for firms with high past
operating income and a Big Four auditor, and, similar to the results in Feng et al., (2015),
27
future operating income is decreasing in capital intensity (PPE) and sales growth
(GROWTH).27
[Table 6]
The Balance of Shareholder Power and the Effectiveness of Internal Controls
In this section, we examine how the balance of shareholder power influences the
effectiveness of internal controls. Prior research has suggested that the primary agency
problem in China is the risk that controlling shareholders expropriate resources at the cost of
minority shareholders (Jiang et al. 2010). Thus, we explore whether internal controls are
more effective in curbing corrupt behavior when there is a greater balance of power among
major shareholders. In particular, we partition firms by the proportion of shares held by the
second to the fifth shareholders. A higher value would suggest that there is a greater balance
of power among major shareholders. In these firms, we expect internal controls to be more
effective, because the board initially sets more rigorous controls, and/or because controlling
shareholders and management are less able or willing to override these controls. In sum, we
expect internal controls to be more effective in curbing corrupt behavior when there is a
greater balance of power among shareholders.
For our full sample, the mean (median) shares owned by the second to fifth largest
shareholders is 10.4% (7.5%) (Table 2 Panel A). This percentage is significantly lower for
stated-owned firms (mean = 10.0% and median = 6.6%), compared with that of non-statedowned firms (mean = 11.2% and median = 9.3%) (Table 2 Panel B). To examine whether the
effectiveness of ICFR is higher when there is a greater balance of power among major
27
The results are similar when we estimate Equation (4) separately for SOEs and non-SOEs; that is, for both
SOEs and non-SOEs, OTHREC, RELATEDLOAN, and EXPOSED all have a significantly negative association
with one-year-ahead operating income.
28
shareholders, we first create an indicator variable TOP2to5IND that takes the value of one if
the ownership by the second to fifth largest shareholders is greater than or equal to the full
sample median (i.e. 7.5%), and zero otherwise. We then add TOP2to5IND and the interaction
term, ICFR×TOP2to5IND, to Equations (1)-(3) and repeat our main analyses. We expect a
negative coefficient on ICFR×TOP2to5IND if internal controls are more effective in curbing
corrupt behavior when TOP2to5 is above the median.
The results are reported in Table 7. As shown in Panel A of Table 7, for the full
sample we find that ICFR×TOP2to5IND is significantly negative in the regressions of
OTHREC and RELATEDLOAN, but not in the regressions of PRIVATE and EXPOSED.
Turning to Panel B of Table 7, when we consider SOEs and non-SOEs separately, we find
ICFR×TOP2to5IND is significantly negative across all measures of corruption within the
non-SOE sample. Each of the coefficients on ICFR×TOP2to5IND, however, is insignificant
within the SOE sample across all four measures of corruption. The differences between the
coefficients of ICFR×TOP2to5IND for the SOE and non-SOE samples are also statistically
significant across all four corruption measures. Overall, these results are consistent with our
prediction that internal controls are more effective when there is a greater balance of power
among the major shareholders. That we only observe this relation within non-state-owned
firms is consistent with our H2.28
[Table 7]
28
An alternative partitioning variable is family firms, which comprise 80 percent of the non-SOEs in our sample.
We do not focus on this partition for two reasons. First, given the high frequency of family firms, this partition
would likely lack sufficient variation to be meaningful. Second, the variation we do examine (shareholdings by
the second to fifth shareholder) also serves as a partitioning variable within family firms.
29
Corroboration of Governmental Pressure on SOEs
Our earlier results are consistent with H2 that internal controls are not as effective in
curbing corporate corruption within SOEs compared to non-SOEs. We motivate this
hypothesis by positing that SOE management have different incentives to adopt internal
control practices than non-SOE management, resulting in internal controls in SOEs that are
effectively “window-dressing.” We provide preliminary support of this in Appendix D where
SOEs tend to have stronger ICFR than non-SOEs, after controlling for known determinants of
ICFR. To provide further evidence on this, we examine whether internal control strength is
associated with the likelihood of CEOs receiving promotions. We identify 788 CEO
turnovers and estimate the following model, where the dependent variable is PROMOTION.
PROMOTION= β0 +β1ICFR +β2SALE +β3 OCF +β4ROA +β5LEV +β6MB +β7CEOAGE
+β8EDUCATION +β9TENURE +β10TOP1SHR +β11MARKETIZATION + ε
(5)
We consider the CEO as having been promoted if in the year subsequent to her
departure she became (1) the Chairman of the board at the same company, (2) the CEO or the
Chairman/Vice Chairman of the board in the parent company, (3) the CEO at a larger public
company where the assets of the new company are at least 20% larger than the former
company, or (4) a high-ranking government official.29
We first estimate Equation (5) using an Ordered Logit regression including all firmyears. The dependent variable takes the value of one if the CEO is promoted in the following
year, zero if there is no CEO turnover, and –1 otherwise. Next we estimate a Logit regression
including only firm-years with CEO turnovers. The dependent variable takes the value of one
29
Executive positions at Chinese SOEs tend to have an “equivalent rank” as that of positions within the
government, which allows us to assess whether a new governmental position is a promotion. For example,
Yongkang Zhou, CEO of Chinese Petroleum had an equivalent rank of the Vice Minister of the State Owned
Assets and Resources from 1996 to 1998. In 1998, he was promoted to the Minister of the State Owned Assets
and Resources.
30
if the CEO is promoted in the following year, and zero otherwise. The results and the
definitions of control variables are presented in Table 7. For the full sample, the coefficient
on ICFR is significantly positive (p-value=0.016). The coefficient on ICFR is significantly
positive (p-value=0.026) for SOEs; in contrast, ICFR is not associated with PROMOTION
within non-SOEs.30 We find similar results using a Logit estimation within firms with CEO
turnover. This finding suggests implementing strong internal controls improves career
outcomes for SOE management. This is consistent with SOE management experiencing
governmental pressure to establish certain internal control procedures, and corroborates our
earlier results suggesting that the multiple objectives of SOE CEOs likely contribute to less
effective internal controls (form over substance).
[Table 8]
Endogeneity of Internal Controls
In this section we discuss the potential endogeneity issue that arises because internal
controls are not exogenous; instead managers and the board of directors choose to establish
internal control policies and procedures. Managers with a tendency to misappropriate assets
may be more likely to choose weaker internal controls. To shed some light on the direction of
causality, we regress all four lagged corruption variables on current ICFR and find no relation,
mitigating the concern of reverse causality (p-values ranging from 0.189 to 0.829; not
tabulated). We then repeat our main analyses on corruption using last-year’s ICFR instead of
current year’s ICFR. We continue to find a significant negative association between lagged
ICFR and corruption using OTHREC, RELATEDLOAN, and PRIVATE, and the effects
30
We note that PROMOTION for SOEs does not have the exact same meaning as PROMOTION for non-SOEs
because only SOEs’ CEOs have the opportunity to be promoted to positions as government officials.
31
continue to be stronger among non-SOEs than SOEs. Collectively, these additional results
support our inferences that the strength of internal controls over financial reporting curbs
corporate corruption, but internal controls within state-owned enterprises are less effective.31
In addition, the choice of internal controls is affected by firm characteristics such as
firm complexity and available resources. It is also possible that both corruption and internal
controls stem from firm characteristics, but that internal controls, per se, would not curb
corruption. One obvious correlated omitted variable might be China’s crackdown on
corruption over this time period. Were it the government, rather than ICFR curbing
corruption, we would expect it to either be pervasive across SOEs and non-SOEs, or be
concentrated among SOEs, where the government has the greatest influence. Instead we find
that ICFR curbs corruption more among non-SOEs. Further, our inclusion of year fixed
effects should also help to capture China’s recent focus on curbing corruption. To investigate whether endogeneity is a concern more generally, we conduct a
Hausman test following Larcker and Rusticus (2010) to test whether endogeneity is likely to
exist. Specifically, we include both ICFR and the residual from the determinants model as
described in Appendix D (the first stage model) in the second stage estimation of corruption
(Equations 1–3). We find that the residual is significant in explaining each of our corporate
corruption variables except for EXPOSED. Thus the test rejects the null of no endogeneity.
31
To further alleviate the concern of unidentified correlated omitted variables, we conduct a survey questioning
management as to the link between internal controls and resource extraction. We randomly select 50 firms from
our sample (24 SOEs and 26 non-SOEs) and administer a survey through the Accounting Office of the
Shenzhen Stock Exchange. Our survey response rate is 100%. When asked whether effective internal controls
can curb corrupt management behavior, 72 percent of the respondents strongly agreed. We do not find a
statistically significant difference in the responses between SOEs and non-SOEs. This response is consistent
with the notion that strong internal controls curb corporate corruption and reduce resource extraction. We view
that this as complementing our empirical analyses and alleviating the concern about correlated omitted variables.
32
We next follow prior research and employ a two-stage least-squares (2SLS) estimation
procedure (e.g., Gul et al. 2009).
Our instrumental variable (or exclusion restriction variable per Lennox, Francis, and
Wang (2012)) in the first stage regression is LIST2006, which we expect to be significantly
associated with ICFR but for which we do not have ex ante reasons to believe would directly
affect corporate corruption (except indirectly through ICFR). Statistically, in the first stage
regression, we find that internal control strength is significantly associated with LIST2006.
When we include LIST2006 in the second stage regressions, it is insignificant in explaining
our three corruption variables. Therefore, our instrumental variable appears to be
significantly correlated with ICFR, but related to our corporate corruption variables mainly
through ICFR. As pointed out in Lennox et al. (2012), results based on the two-stage
approach tend to be sensitive to the choice of the instrumental variable; in many cases the
OLS approach is preferable. Therefore, we consider OLS in our main analysis but also
confirm that our results are robust to using the two-stage approach.
We repeat all of our analyses using the predicted internal control scores from the first
stage regression and report the summary of results in Table 8. Our results are substantively
similar for all four proxies of corporate corruptions (OTHREC, RELATEDLOAN, PRIVATE
and EXPOSED); that is, firms with higher internal control scores have fewer other
receivables, loans issued to related parties, and fewer expenses susceptible to private
consumption for the full sample, and the association is significantly weaker within SOEs
When examining EXPOSED as the dependent variable, we again only find evidence that
internal controls are negatively associated with ex post disclosures of corrupt behavior within
33
non-SOEs. Taken together, these findings mitigate the concern that a particular firm feature
drives both corruption and internal control strength, but that internal control strength alone
would not curb corruption.
[Table 9]
Robustness Checks
We conduct two robustness checks. We first consider whether the inclusion of
internal controls related to other aspects but not financial reporting affects our results. To do
so we select items related to internal control procedures that are not related to financial
reporting (e.g., whether the firm has a legal counselor) and use the weighted average as the
measure of internal control unrelated to financial reporting (ICDIFF). We then include
ICDIFF as an additional control in our main regressions of corruption (Equations 1 – 3).
Similar to our main results, we find the coefficient on ICFR remains significantly negative
for the regressions of OTHREC, RELATEDLOAN, and PRIVATE (both full and non-SOE
samples) and EXPOSED (non-SOE sample only), while the coefficient on ICDIFF is
insignificant in these regressions for the full sample and both sub-samples. The lack of result
for ICDIFF could be driven by the fact that firms exhibiting strong internal controls over
financial reporting tend to have strong other internal controls, which is evident by a high
correlation of 43% between ICDIFF and ICFR. Overall our results are similar when we
control for internal controls other than financial reporting.
Next, we investigate whether the effect of internal control strength on corporate
corruption varies by the type of internal control procedures. We split the components in our
ICFR measure into controls related to operations (ICFR_OPERATIONS) (e.g., controls for
authorizing and approving) and controls related to compliance with regulations
34
(ICFR_COMPLIANCE) (e.g., having a system governing information disclosures). We
identify 10 out of the 64 components that are more related to compliance than operations. We
note that in many cases it is difficult to separate compliance controls from operational
controls, and they are likely highly correlated. In fact, the Pearson correlation between these
two types of controls is 0.508. We replace ICFR with ICFR_OPERATIONS and
ICFR_COMPLIANCE in our main regressions (Equations 1 – 3). Similar to our main results,
we find that, across all four corruption measures, firms with strong operational internal
controls exhibit less corruption, and this association is concentrated within non-state-owned
firms. The coefficient estimates on ICFR_COMPLIANCE, however, are insignificant across
all specifications. This result suggests that the effect of internal control strength on corporate
corruption is primarily driven by operational controls.
VI.
CONCLUSION
We examine the relation between the strength of internal control over financial
reporting and corporate corruption. Using a sample of Chinese firms, we find that after
controlling for known determinants of corruption and internal control strength, firms with
strong internal controls exhibit significantly less corporate corruption. Specifically, high
internal control scores are associated with significantly less tunneling of resources out of the
firms and lower travel and entertainment costs (our proxy for private consumption expense
reimbursement).
We also use the setting of state ownership to explore window dressing (form over
substance) in the internal control arena. Because SOEs experienced intense governmental
pressure to establish internal controls, we expect that these internal controls will be more
35
likely to be form over substance, as the management was pressured to adopt the policies.
Thus, we explore the differences in the effectiveness of controls in mitigating corruption
between SOEs and non-SOEs. We find that the state ownership of Chinese firms significantly
weakens the effect of internal control strength on the extent of corporate corruption in the
forms of tunneling resources and private consumption. In addition, we find that internal
control strength has a significant negative association with the frequency of disclosed
corruption within non-state-owned firms, but not state-owned firms. This finding is consistent
with state-owned enterprises adopting internal control policies and procedures in form, but
not substance.
In summary, our results suggest that, on average, strong internal controls seem to
reduce the extent of corporate corruption; however, institutional factors and the associated
incentives also play a significant role in the effectiveness of internal controls. Internal control
systems that are only form, without substance, are less effective.
36
References
Allen, F., J. Qian, and M. Qian. 2005. Law, finance, and economic growth in China, Journal
of Financial Economics 77, 57–116.
Ashbaugh-Skaife, H., D. Collins, and W. Kinney. 2007. The discovery and reporting of
internal control deficiencies prior to SOX-mandated audits, Journal of Accounting and
Economics 44, 166–192.
Ashbaugh-Skaife, H., D. Collins, W. Kinney, and R. LaFond. 2008. The effect of internal
control deficiencies and their remediation on accrual quality. The Accounting Review 83(1),
217–250.
Cai, H., H. Fang, and L. Xu. 2011. Eat, drink, firm and government: An investigation of
corruption from the entertainment and travel costs of Chinese firms. Journal of Law and
Economics 54, 55–78.
Chen, H., W. Dong, H. Han, and N. Zhou. 2013. A comprehensive and quantitative internal
control index: construction, validation, and impact. Working paper, Xiamen University,
Zhejiang University, and State University of New York at Binghamton.
Chen, D., X. Chen, and H. Wan. 2005. Compensation regulation in SOEs and excessive
consumption of perquisites. Economics Research (Chinese) 2005(2).
Chen, S., S. Sun, and D. Wu. 2010. Client importance, institutional improvements, and audit
quality in China. The Accounting Review 85 (1), 127–158.
Cheng, M., D. Dhaliwal, and Y. Zhang. 2013. Does investment efficiency improve after the
disclosure of material weaknesses in internal control over financial reporting? Journal of
Accounting and Economics 56, 1–18.
Clogg, C., E. Petkova, and A. Haritou. 1995. Statistical methods for comparing regression
coefficients between models. American Journal of Sociology 100 (5), 1261–1293.
Dechow, P., R. Sloan, and A. Hutton. 1996. Causes and consequences of earnings
manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary
Accounting Research 13, 1–36.
Djankov, S., R. La Porta, F. Lopez-de-Silanes, and A. Shleifer. 2008. The law and economics
of self-dealing. Journal of Financial Economics 88, 430–465.
Donelson, D., M. Ege, and J. McInnis. 2014. Internal control weakness and financial
reporting fraud. Working paper. University of Texas and University of Florida.
37
Doyle, J., W. Ge, and S. McVay. 2007a. Accruals quality and internal control over financial
reporting. The Accounting Review 82, 1141–1170.
Doyle, J., W. Ge, and S. McVay. 2007b. Determinants of weaknesses in internal control over
financial reporting. Journal of Accounting and Economics 44, 193–223.
Fan, G., and X. Wang. 2006. In: The report on the relative process of marketization of
regions in China. The Economic Science Press, Beijing (in Chinese).
Fan, J., T. J. Wong, and T. Zhang. 2007. Politically connected CEOs, corporate governance,
and post-IPO performance of China’s newly partially privatized firms. Journal of Financial
Economics 84, 330-357.
Feng, M., C. Li, and S. McVay. 2009. Internal control and management guidance. Journal of
Accounting and Economics 48, 190–209.
Feng, M. C. Li, S. McVay and H. Skaife. 2015. Does ineffective internal control over
financial reporting affect a firm’s operations? Evidence from firms’ inventory management.
The Accounting Review 90(2), 529–557.
Gul, F., S. Fung, and B. Jaggi. 2009. Earnings quality: some evidence on the role of auditor
tenure and auditors’ industry expertise. Journal of Accounting and Economics 47, 265-287.
Jiang, G., C. Lee, and H. Yue. 2010. Tunneling through intercorporate loans: the China
experience, Journal of Financial Economics 98, 1–20.
Kothari, S., A. Leone, and C. Wasley. 2005. Performance matched discretionary accrual
measures. Journal of Accounting and Economics 39, 163–197.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., and R. Vishny. 2000. Investor protection and
corporate governance. Journal of Financial Economics 58, 3–27.
Larcker, D. and T. Rusticus. 2010. On the use of instrumental variables in accounting
research. Journal of Accounting and Economics 49(3), 186-205.
Lennox, C., Francis J. and Z. Wang. 2012. Selection models in accounting research. The
Accounting Review 87 (2), 589-616.
Leuz, C., Nanda, D., and P. Wysocki. 2003. Earnings management and investor protection:
An international comparison. Journal of Financial Economics 69, 505-527.
Lin, J., F. Cai and Z. Li. 1998. China’s economic reforms: some unfinished business.
American Economic Review 88(2), 422–427.
38
Lin, K., J. Piotroski, Y.G. Yang, and J. Tan. 2012. Voice or exit: Independent director
decisions in an emerging economy. Working paper.
Liu Q., L. Luo, W. He, and H. Chen. 2012. State ownership, institutional environment and
internal control. Accounting Research (Chinese) 3, 52-61.
Marinovic, I. 2013. Internal control system, earnings quality, and the dynamics of financial
reporting. RAND Journal of Economics 44(1), 145–167.
Okeeffe, K. 2014. Macau Gambling Revenue Falls 23% in October. Wall Street Journal.
http://online.wsj.com/articles/macau-gambling-revenue-falls-23-in-october-1415079292
Piotroski, J., and T. J. Wong. 2012. Institutions and information environment of Chinese
listed firms. Chapter from Capitalizing China (NBER), 201-242.
Piotroski, J., T.J. Wong, and T. Zhang. 2015. Political incentives to suppress negative
financial information: evidence from Chinese listed firms. Journal of Accounting Research
53(2), 405-459.
Skaife, H., D. Veenman, and D. Wangerin. 2013. Internal control over financial reporting and
managerial rent extraction: Evidence from the profitability of insider trading. Journal of
Accounting and Economics 55, 91-110.
Shleifer, A. and R.W. Vishny. 1993. Corruption. The Quarterly Journal of Economics, 599–
617.
39
Appendix A: Overall Internal Control Index
This appendix provides a brief summary of the overall internal control index. For
details about the index, please see Appendix 1 in Chen et al. (2013). A research team led by
Professor Henwen Chen at the Xiamen University in China created the internal control
index. The construction of the internal control index is based on the Basic Standards and the
Guidelines for Chinese public companies,32 and the requirements issued by the Shenzhen and
Shanghai Exchanges. 33 The design of the index follows the Internal Control—Integrated
Framework (released by the Committee of Sponsoring Organizations of the Treadway
Commission in 1992). Under this framework, the internal control index contains five
components: control environment, risk assessment, control activities, information and
communication, and monitoring. The COSO framework has gained acceptance by regulators,
such as the SEC and PCAOB, and forms the foundation of SOX 404.
The researchers adopted the analytic hierarchy process (AHP) developed by Thomas
L. Saaty in the 1970s.34 Internal control index begins with five first-level items, the same as
the five components in the COSO framework. Each of the first-level items then contains more
evaluation items. For example, the codes for the first item in the first three levels are IC1,
IC11 and IC111, respectively. For the fourth level, the code is defined as, for example,
IC11101. The first three numbers (111) represent the first three levels to which this item is
affiliated. If there is no third level for a particular item, then the first two numbers are
presented. Overall, there are five first-level items, 24 second-level items, 43 third-level items,
and 144 fourth-level items.
In evaluating each of the 144 items regarding internal controls of a firm, the
researchers collect its internal control information from financial statements, CSRC filings,
government documents, and press releases. In most cases, a value of zero or one is assigned
to a fourth-level item based on the information obtained (see Appendix B for examples). The
researchers follow AHP to assign weights for the items (see Chen et al. for details on
calculating the item weights) and the overall index score is the weighted average of all items.
We identify 64 of the144 items that most directly relate to ICFR. See Appendix B.
32
On July 15, 2006, five Chinese government authorities and regulatory bodies, the Ministry of Finance, the
China Securities Regulatory Commission, the National Audit Office, the China Banking Regulatory
Commission, and the China Insurance Regulatory Commission jointly established a Committee aiming to
stipulate a set of universal, recognizable, and scientific rules governing firms’ internal controls. After two years
of conducting research and seeking feedback, the Committee on Internal Control Standards issued The Basic
Standards of Enterprise Internal Controls (the Basic Standards) requiring that a listed firm issue a selfassessment of its internal controls and that a Certified Public Accountant issue a report on the firms’ internal
controls. Later, Supplemental Guidelines of Firms’ Internal Controls (the Guidelines) was released on April 26,
2010. The Basic Standards became effective on January 1, 2011 for firms listed both on an exchange in China
and another exchange abroad (dual-listed), and on January 1, 2012 for firms listed on the Shanghai Stock
Exchange or the main section of the Shenzhen Stock Exchange.
33
In July 2006, the Shanghai Stock Exchange (SSE) issued A Guideline of Internal Controls for Listed Firms on
Shanghai Stock Exchange. Further, in September 2006, the Shenzhen Stock Exchange issued A Guideline of
Internal Controls for Listed Firms on Shenzhen Stock Exchange.
34
AHP decomposes a complex decision problem into a hierarchy of more easily comprehensible sub-problems,
and then produces the qualitative and quantitative analyses of each sub-problem. AHP also makes pairwise
comparisons of the same-level items in every sub-hierarchy, analyzing their relative impact on an element in the
hierarchy above them.
40
Appendix B: Composition of the Internal Control over Financial Reporting Measure
Out of the 144 items of the internal control index, we select only those items that apply to
internal control over financial reporting. We remove 68 items that are not directly related to
internal control but instead related to corporate governance (e.g., percentage of institutional
shareholders), or items reflecting the outcome of internal controls (e.g., whether the company
reported inventory damages in the year). We classify another 12 items as relating to overall
internal controls but not financial reporting. As a result, our ICFR score consists of 64
evaluation items. The following provide a complete list of these components. Standardized
values are calculated as the actual value for the item scaled by the maximum value of the same
item across all firms in the dataset. We note these as “to be standardized” in the following
list. Since each third-level index evaluate a specific aspect, we first take the mean of the items
under the same third-level index (second-level if there is no third-level), and then we take the
mean over the third-level index as our measure of IFCR. For example, we will first take the
average of IC11101, IC11102, and IC11103 as the score for IC111. Then ICFR is calculated
using the mean of scores of all the third-level (second-level if there is no third-level) index
items. As an alternative measure, we also calculate ICFR as the simple mean of the 64 fourthlevel items. Our results remain unchanged using this alternative measure.
ICFR Component (fourth level items)
IC1: Control Environment
IC111: Institutional Arrangement
IC11101: Existence of a manual or guideline
on internal control in the company.
IC11102: Use of an outside professional to
help the company improve its internal
control.
IC113: Board of Directors
IC11303: A board-level committee charged
with the oversight of internal control (audit
committee or risk management committee).
IC11304: Number of audit committee
members
IC114: Board of Supervisors
IC11404: Supervisors with legal and
accounting expertise.
IC121: Internal Control Implementation
IC12101:Existence of an internal control
department in the company.
IC12102:The internal audit department
reports to the board.
IC131: Recruiting and Training
IC13102: The human resource policy
contains provisions on recruiting.
IC13103: The human resource policy
contains provisions on training.
41
Value
Example
Mean
1 for yes, 0 for no
1.00
0.954
1 for yes, 0 for no
1.00
0.056
1 for yes, 0 for no
1.00
0.870
To be
standardized
3.00
0.380
1 for yes, 0 for no
0.00
0.094
1 for yes, 0 for no
1.00
0.817
1 for yes, 0 for no
1.00
0.681
1 for yes, 0 for no
1.00
0.379
1 for yes, 0 for no
1.00
0.421
IC13104: The human resource policy
contains provisions on job rotations at critical
levels.
IC132: Incentives
IC13202: The human resource policy
contains provisions on evaluations.
IC13205: Employee salary is linked to
performance.
IC133: Severance
IC13301: The human resource policy
contains provisions on discharging
employees.
IC13302: The human resource policy
contains provisions on employee resignations.
IC13303: An audit is performed upon the
departure of the Chairman, CEO, and other
executives.
IC142: Competence variable mean
IC14201: Percentage of the staff with at least
a junior college education.
IC14202: Existence of employee training by
the company.
IC2: Risk Assessment
IC221: Risk evaluation
IC22101: Existence of a risk management
committee or department.
IC222:Internal risk
IC22202: A human resource risk disclosed in
public filings.
IC22203: An operation risk disclosed in
public filings.
IC22205: A financial risk disclosed in public
filings.
IC231: Risk Evaluation Method variable mean
IC23101: A quantitative risk analysis in the
annual report or in the internal control
evaluation report.
IC23102: Economic risk disclosed?
IC241: Response Strategies
IC24101: Any response strategy disclosed?
IC24102: Any analysis of risk tolerance?
IC242: Risk Management Measure
IC24201: A risk management measure taken
to control risk.
42
1 for yes, 0 for no
0.00
0.079
1 for yes, 0 for no
1.00
0.831
1 for yes, 0 for no
1.00
0.837
1 for yes, 0 for no
0.00
0.124
1 for yes, 0 for no
0.00
0.062
1 for yes, 0 for no
0.00
0.123
To be
standardized
0.66
0.395
1 for yes, 0 for no
1.00
0.609
1 for yes, 0 for no
1.00
0.064
1 for yes, 0 for no
0.00
0.112
1 for yes, 0 for no
1.00
0.303
1 for yes, 0 for no
0.00
0.465
1 for yes, 0 for no
0.00
0.032
1 for yes, 0 for no
0.00
0.078
1 for yes, 0 for no
1 for yes, 0 for no
1.00
1.00
0.721
0.524
1 for yes, 0 for no
1.00
0.228
IC3: Control Activities
IC31: Separation of Duties
IC31101: Controls for incompatible
separation of duties.
IC31102: Controls for authorizing and
approving.
IC31103: Approval of material matters is in
accordance with existing procedures.
IC32: Accounting Control
IC32101: Company has an accounting
information system.
IC34: Budget Control variable
IC34101: Existence of a budgeting committee
or department.
IC34102: A budget is implemented in the
company.
IC34103: The annual budget is discussed in
the shareholder meeting or other similar
setting.
IC35: Operating Control
IC35101:Existence of an operational
analysis.
IC36: Performance Control
IC36101:Existence of a performance
analysis committee or department.
IC36102:A report is issued from the
performance analysis.
IC37: Emergency Control
IC37101:Existence of a system that
generates early warnings for material risk.
IC37102:Existence of an emergency
response system.
IC4: Information and Communication
IC41: Information Collection
IC41101:Existence of a channel for internal
communication of information (e.g. financial
analysis and staff meeting)
IC41102:Channel for external collection of
information (e.g. information from regulatory
bodies).
IC42: Information Communication
IC42201: A system/mechanism governing
information disclosure.
IC42205: Another platform for
communicating with investors.
43
1 for yes, 0 for no
1.00
0.485
1 for yes, 0 for no
1.00
0.670
1 for yes, 0 for no
1.00
0.653
1 for yes, 0 for no
1.00
0.385
1 for yes, 0 for no
1.00
0.058
1 for yes, 0 for no
1.00
0.829
1 for yes, 0 for no
1.00
0.267
1 for yes, 0 for no
1.00
0.996
1 for yes, 0 for no
1.00
0.964
1 for yes, 0 for no
1.00
0.870
1 for yes, 0 for no
1.00
0.109
1 for yes, 0 for no
1.00
0.206
1 for yes, 0 for no
1.00
0.994
1 for yes, 0 for no
1.00
0.995
1 for yes, 0 for no
1.00
0.983
1 for yes, 0 for no
1.00
0.398
IC425: Internal Timeliness
IC42501:Periodic reports are released on the
scheduled date.
IC43: Information System
IC43101:Existence of an information
department or information security
department.
IC441: Anti-Fraud Mechanism
IC44101:Anti-fraud mechanisms in the
company.
IC44102:A channel for whistle blowing.
IC442: Anti-Fraud Priority
IC44201:The company specifies the
priorities for anti-fraud.
IC5: Monitoring
IC51: Internal Monitoring Function
IC511: Monitoring from the Internal Audit
Department
IC51101:Inspection of internal control by
the internal audit department.
IC512: Monitoring from the Board of
Supervisors
IC51201:The board of supervisors monitors
operational activities, such as financing
activities, asset sales, and acquisitions.
IC513: Monitoring from the Board of
Directors
IC51301:The audit committee discusses the
internal control inspection in its responsibility
report.
IC51302:The independent directors discuss
the internal control inspection in their
responsibility report.
IC514: Special Monitoring
IC51401:Monitoring for suspicious special
events.
IC52: Internal Control Deficiencies
IC52101:A standard for deficiency
recognition.
IC52102:An analysis of the reasons for
deficiencies.
IC52103:A plan for rectifying internal
control deficiencies.
IC52104:The company tracks the reform of
internal control procedures.
44
1 for yes, 0 for no
1.00
0.918
1 for yes, 0 for no
1.00
0.124
1 for yes, 0 for no
0.00
0.068
1 for yes, 0 for no
0.00
0.079
1 for yes, 0 for no
0.00
0.035
1 for yes, 0 for no
1.00
0.688
1 for yes, 0 for no
1.00
0.982
1 for yes, 0 for no
0.00
0.317
1 for yes, 0 for no
0.00
0.269
1 for yes, 0 for no
1.00
0.427
1 for yes, 0 for no
0.00
0.052
1 for yes, 0 for no
0.00
0.305
1 for yes, 0 for no
0.00
0.523
1 for yes, 0 for no
0.00
0.582
IC53: Internal Control Disclosure
IC53102:The internal controls are well
designed.
IC53103:The internal controls function well.
IC53104:Performance of an inspection of
the internal control system.
IC52106:The inspection of the internal
control system was evaluated.
IC53107:A plan to improve internal control
exists.
IC53108:A plan for next year’s internal
control exists.
IC53110:The board of supervisors evaluates
the internal control system.
IC53111:The independent directors evaluate
the internal control system.
45
1 for yes, 0 for no
1.00
0.525
1 for yes, 0 for no
1 for yes, 0 for no
1.00
1.00
0.525
0.443
1 for yes, 0 for no
0.00
0.317
1 for yes, 0 for no
1.00
0.361
1 for yes, 0 for no
0.00
0.084
1 for yes, 0 for no
1.00
0.682
1 for yes, 0 for no
0.00
0.395
Appendix C: Variable Definitions
INTERNAL CONTROL MEASURES
A measure of the strength of internal control over financial reporting in
ICFR
year t based on 64 of the 144 items collected by Chen et.al. (2013); see
Appendices A&B for details.
DEPENDENT VARIABLES
Evidence of tunneling (removal of cash from the firm), measured as
OTHREC
other receivables scaled by year-end total assets;
Evidence of tunneling (removal of cash from the firm), measured as the
RELATEDLOAN
amount of loans from the listed firm to related parties disclosed in the
notes to the financial statements, scaled by year-end total assets;
Private consumption expenses scaled by sales, including specific
PRIVATE
expenditures on office supplies, business travel, entertainment, telecommunication, training abroad, board meetings, automobile,
conferences, and other expense. Data on these expenditures are
collected from note disclosures.
An indicator variable equal to one if there is ex post public disclosure of
EXPOSED
corrupt self-serving behavior by management (e.g. receipt of bribes) in
year t (where year t is the year management undertook the corrupt
behavior, not necessarily the year it was revealed), zero otherwise;
Evidence of earnings management, measured using the absolute value
ABSDA
of discretionary accruals in year t based on the performance-adjusted
cross-sectional modified Jones model (Kothari et al. 2005);
Evidence of earnings management, measured as an indicator variable
RESTATE
equal to one if there is a restatement of year t (discovered in subsequent
years), zero otherwise;
Operating income divided by average total assets;
OI
CONTROL VARIABLES
ASSETS
The logarithm of total assets (in Chinese Yuan);
Total sales in year t divided by average total assets.
ATO
An indicator variable, equal to one if the company is audited by one of
BIG4
the four largest U.S. audit firms, zero otherwise;
The average of five components related to characteristics of the board
BOARD
of directors collected by Chen et al. (2013): board size, number of board
committees, percentage of independent directors, average attendance
ratio of board meetings for independent directors, and the number of
dissenting proposals by independent directors. Each component is
standardized to range from zero to one (i.e., by scaling by the maximum
value of that component) before averaging.
An indicator variable equal to one if the CEO is also the Chairman of
CEOCHAIR
the board, zero otherwise;
Percentage change of the amount of total debt;
DISSUE
Percentage change of the number of outstanding shares;
EISSUE
An indicator variable equal to one if the company is traded on the
EXCHANGE
Shenzhen Stock Exchange, zero if the company is traded on the
Shanghai Stock Exchange;
An indicator variable equal to one if the company exports merchandise
EXPORT
to foreign countries and districts (including Hong Kong and Taiwan),
zero otherwise;
46
FOREIGHSHR
Percentage of foreign institutional ownership;
The average of four components related to shareholder composition
collected by Chen et al. (2013): Chairman of the board is also the
controlling shareholder (1 for no, 0 for yes), the ownership percentage
of institutional investors, the number of institutional investors in list of
top ten shareholders, and the average percentage of shareholders that
attend annual shareholder meetings. Each component is standardized to
range from zero to one (by scaling each value by the maximum value of
that component) before averaging.
An indicator variable equal to one if the company lists on the Shenzhen
LIST2006
or Shanghai Exchanges after 2005, zero if they were already listed;
An indicator variable equal to one if the company reports a loss in the
LOSS
year, zero otherwise;
An indicator variable equal to one if the company completed a merger
M&A
or acquisition during the year, zero otherwise;
MARKETIZATION A comprehensive index measuring the development of the regional
market in which a firm is registered based on Fan and Wang (2006) and
Jiang et al. (2010);
Market value of equity divided by book value of equity.
MB
Percentage of outstanding shares held by top executives (multiplied by
MGMTSHR
100);
Ratio of the average executive total compensation relative to other
PAYRATIO
employees’ average total compensation;
Earnings before extraordinary items divided by average total assets;
ROA
The logarithm of total sales (in Chinese Yuan);
SALES
The logarithm of the number of the geographic segments;
SEGMENTS
An indicator variable equal to one if the company is controlled by the
SOE
central or local government or their agencies, zero otherwise;
The percentage of outstanding shares held by the largest shareholder;
TOP1SHR
The sum of the ownership percentages of the second to the fifth largest
TOP2to5
shareholders;
An indicator variable, equal to one if the sum of the ownership
TOP2to5IND
percentages of the second to the fifth largest shareholders is above or
equal to the median,and zero otherwise.
INVCOMP
47
Appendix D: Determinants and validation of ICFR Score
In this appendix we first explore the determinants of our ICFR Score to assess
whether it exhibits similar determinants as previously examined measures of ICFR strength
(Section D1). We next examine the association between the internal control score and
earnings quality as a validity test of our internal control measure (Section D2).
D.1
Determinants of ICFR Score
We develop our internal control prediction model based on determinants considered
in the U.S. literature as well as factors specific to the China market. Specifically, we estimate
the following first stage regression:
ICFR = β0 + β1SOE +β2ASSETS + β3ROA + β4 LOSS + β5 MB + β6 EXPORT
+ β7 SEGMENTS + β8 M&A + β9 FOREIGNSHR + β10BIG4+ β11LIST2006
(D1)
+ β12 MARKETIZATION + β13 EXCHANGE + ε
Our first determinant is SOE, which equals one if the controlling shareholder of a firm is
either a central or local government or their agencies. The inclusion of this variable is based
on our lengthy discussion above regarding the governmental pressure on SOE firms to
establish internal controls. We next follow prior literature and include the following variables
that have been shown to determine a company’s internal control strength. Doyle et al. (2007b)
find that internal control problems are more prevalent in firms that are smaller, financially
weaker, growing more quickly, and more complex. They argue that larger firms and more
profitable firms have more resources to invest in internal control, thus we include firm size
(ASSETS), and return on assets (ROA), and expect the strength of internal controls to be
increasing with both ASSETS and ROA. Similarly, firms going through financial distress are
less likely to invest resources in internal control. We include LOSS that equals one if the
company reports a loss in the year to proxy for poor financial health. We include the ratio of
the market value of equity to book value of equity (MB) to proxy for growth. We measure
financial reporting complexity by the existence of exporting transactions (EXPORT) and the
number of segments (SEGMENTS). Ashbaugh-Skaife et al. (2008) find that structural
changes within a company could affect the internal control strength, thus we include an
indicator variable M&A that equals one if a firm completes a merger or acquisition during the
year. As suggested in Ashbaugh-Skaife et al. (2007), shareholder composition and auditors
may also influence a firm’s choice in its internal control. We include the percentage of shares
held by foreigners and expect that a greater concentration of foreigners would reflect greater
demand for strong oversight and thus internal controls. We include an indicator variable
equal to one if the company is audited by one of the four biggest U.S. accounting firms to
proxy for auditor oversight.
In addition to the determinants established in prior research in the U.S., we include
the following variables specific to Chinese firms’ internal control strength. First, 2006 was
the year China moved to advance corporate internal control. In 2006 both the Shenzhen and
Shanghai exchanges issued several regulations regarding internal control for firms going
48
public. 35 As a result, any firm going public in 2006 or afterwards is subject to higher
regulatory standards for internal controls. Therefore, we include an indicator variable
LIST2006 that takes a value of one for firms that listed on the Shenzhen and Shanghai
exchanges on or after 2006. Second, we recognize the difference in regulations on internal
controls by the two Chinese stock exchanges in our sample (Shanghai vs. Shenzhen) by
including an indicator EXCHANGE that equals one if a firm is listed on the Shenzhen
exchange, and zero otherwise. Finally, we include MARKETIZATION, which measures the
development of the regional market in which the firm is registered (Fan and Wang 2006,
Jiang et al. 2010). We also include year and industry fixed effects. The results are reported in
Table D1.
The results in Table D1 suggest that state-owned enterprises (SOE) tend to have
stronger internal controls; the coefficient on SOE is significantly positive (coefficient=0.010;
p-value=0.010), consistent with SOEs experiencing government pressure to establish internal
controls. Consistent with prior literature using U.S. internal control disclosures, we find that
larger and more profitable firms have higher internal control scores. The coefficient on firm
size (ASSET) is significantly positive. As expected, there is a positive relation between ROA
and ICFR, and the coefficient on LOSS is significantly negative (i.e., loss firms tend to have
weaker internal controls). Unlike studies using U.S. data, we find that more complex firms
(SEGMENTS) tend to have stronger internal controls. It is important to point out that U.S.
studies use the ex post existence of ineffective internal controls to identify internal control
strength, and complexity likely leads to the existence of underlying problems. Our measure,
in contrast, examines the policies and procedures in place, and thus likely reflects that more
complex firms need, and thus choose to implement, stronger internal controls. We find no
evidence that the composition of foreign shareholders is associated with internal control
strength, but we see that companies audited by the Big Four U.S. audit firms tend to maintain
stronger internal controls. Consistent with the higher internal control requirements for firms
that listed on or after 2006, and firms listed on the Shenzhen Stock Exchange, we find
positive coefficients on LIST2006 and EXCHANGE. Finally, we find a significantly positive
association between internal control over financial reporting and the development of regional
markets (MARKETIZATION).
We also estimate Equation (D1) separately within the SOE and non-SOE samples
(excluding SOE as a main effect). We find that the adjusted R-square is lower within SOEs
(27.5%) than non-SOEs (33.2%). The lower explanatory power among SOEs is consistent
with our broader conclusion that ICFR within SOEs may be driven by governmental pressure
incremental to the underlying economic determinants identified in prior research.
D.2: Validating the Measure of ICFR Strength
To the extent that our measure of internal control strength captures the strength of
internal control, we expect to observe a positive association between ICFR and earnings
quality.
35
For example, in the IPO process, a new regulation requires a firm going public to disclose the management’s
assessment as well as the auditor’s opinion on its internal control effectiveness.
49
We examine two measures of earnings quality: the absolute value of discretionary
accruals (ABSDA) and the occurrence of accounting errors (RESTATE).
Earnings Quality = β0 +β1ICFR +β2ASSETS+β3OCF +β4LEV +β5ROA +β6MB
(D2)
+β7EISSUE +β8DISSUE +β9BIG4 +β10TOP1SHR+ ε
The dependent variable is ABSDA or RESTATE. ABSDA is the absolute value of the residuals
from the performance-adjusted cross-sectional modified Jones model (Kothari et al. 2005).
RESTATE equals one if the firm subsequently restated an accounting error made in the
current year, and zero otherwise. We control for variables documented in prior literature that
influence earnings quality including cash flows from operations (OCF), leverage (LEV), firm
accounting performance measured by returns on assets (ROA), and growth opportunities
measured by market-to-book ratio (MB). We control for new issuances of equity and debt
(EISSUE and DISSUE) because firms have stronger incentives to manage earnings in the
presence of financing needs (Dechow et al. 1996). We also include audit quality, which is an
important factor in determining earnings quality; BIG4 is equal to one if the auditor is one of
the four largest U.S. accounting firms, and zero otherwise. Lastly, we include the percentage
of shares owned by the largest shareholder (TOP1SHR) to control for potential effects of
ownership structure on accounting quality (Dechow et al. 1996). Finally we include year and
industry fixed effects.
Overall, we find that ICFR is negatively associated with both the absolute value of
discretionary accruals and the occurrence of accounting errors, suggesting that earnings
quality is lower for firms with weaker internal controls. The coefficients on the control
variables are largely consistent with prior literature.
We also examine the association between ICFR and earnings quality for the SOE
firms and the non-SOE firms separately. When examining discretionary accruals, the
coefficient appears slightly larger in magnitude among non-SOEs, but the significance level
weaks to extremely marginal. When examining restatements we find that, for both SOE and
non-SOE firms, ICFR has a significant negative association. Again, the magnitude of the
coefficient on ICFR for non-SOEs appears larger than that of SOEs, but the difference is not
statistically significant.
Taken together, the findings in Appendix D provide support for the validity of our
internal control strength measure.
50
Table D1: Determinants of Internal Control over Financial Reporting Strength
Intercept
Predicted
Signs
?
SOE
+
ASSETS
+
ROA
+
LOSS
–
MB
–
EXPORT
–
SEGMENTS
?
M&A
–
FOREIGHSHR
+
BIG4
+
LIST2006
+
MARKETIZATION
+
EXCHANGE
?
Year effects
Industry effects
No. of Obs.
Adjusted R2
Full Sample
SOEs
Non-SOEs
-0.118***
(0.004)
0.010**
(0.010)
0.022***
(0.000)
0.038*
(0.068)
-0.018***
(0.001)
0.0003
(0.829)
0.003
(0.794)
0.013***
(0.001)
0.004
(0.880)
-0.007
(0.600)
0.012*
(0.087)
0.043***
(0.000)
0.002**
(0.043)
0.041***
(0.000)
Included
Included
-0.132**
(0.013)
-0.163**
(0.012)
0.021***
(0.000)
0.047
(0.111)
-0.009
(0.106)
0.0001
(0.544)
0.004
(0.772)
0.010**
(0.026)
0.002
(0.693)
0.030
(0.183)
0.008
(0.202)
0.039***
(0.000)
0.004***
(0.001)
0.036***
(0.000)
Included
Included
0.024***
(0.000)
0.013
(0.347)
-0.032***
(0.000)
0.0004
(0.881)
0.000
(0.526)
0.017**
(0.012)
0.008
(0.913)
-0.075
(0.953)
0.013
(0.305)
0.054***
(0.000)
-0.003
(0.944)
0.052***
(0.000)
Included
Included
4,775
0.298
3,196
0.275
1,579
0.332
All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to
mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. Pvalues reported in parentheses are based on one-tailed tests for variables with predicted signs and twotailed tests for variables without predicted signs. Standard errors are clustered by firm.
51
Table D2: Internal Control and Earnings Quality
Dependent Variable
Predicted
Full Sample
Signs
Intercept
?
0.321***
(0.000)
ICFR
-0.034**
(0.035)
ASSETS
-0.013***
(0.000)
OCF
-0.110***
(0.002)
LEV
+
0.043***
(0.000)
ROA
?
0.100**
(0.015)
MB
+
0.0002
(0.276)
EISSUE
+
0.047***
(0.000)
DISSUE
+
0.043***
(0.000)
BIG4
0.016
(0.993)
Top1SHR
-0.004
(0.372)
Year effects
Included
Industry effects
Included
No. of Obs.
Adjusted (or Pseudo) R2
4,735
0.230
ABSDA (OLS)
RESTATE (Logit)
SOEs
Non-SOEs
Full Sample
SOEs
Non-SOEs
0.300***
(0.000)
-0.034*
(0.061)
-0.012***
(0.000)
-0.082**
(0.005)
0.056***
(0.000)
0.042
(0.343)
0.0004
(0.221)
0.055***
(0.000)
0.041***
(0.000)
0.016
(0.996)
0.006
(0.656)
Included
Included
0.475***
(0.000)
-0.046
(0.108)
-0.018***
(0.000)
-0.147***
(0.010)
0.032***
(0.001)
0.177***
(0.009)
-0.0003
(0.648)
0.038***
(0.007)
0.047***
(0.000)
0.019
(0.786)
-0.024
(0.212)
Included
Included
0.420
(0.780)
-1.409**
(0.019)
-0.088
(0.108)
-0.878
(0.120)
0.076
(0.321)
-1.704**
(0.035)
0.012
(0.138)
0.175
(0.231)
-0.027
(0.611)
-1.132**
(0.021)
-0.987**
(0.017)
Included
Included
1.128
(0.577)
-1.141*
(0.088)
-0.131*
(0.088)
-0.607
(0.268)
0.152
(0.310)
-2.530**
(0.030)
-0.006
(0.630)
0.540**
(0.025)
-0.040
(0.632)
-1.531***
(0.006)
-0.438
(0.211)
Included
Included
0.269
(0.915)
-1.978**
(0.041)
-0.054
(0.319)
-0.958
(0.208)
0.006
(0.488)
-0.303
(0.793)
0.029**
(0.019)
-0.356
(0.775)
0.022
(0.444)
0.034
(0.514)
-3.172***
(0.001)
Included
Included
3,176
0.230
1,559
0.216
4,760
0.055
3,185
1,575
0.065
0.068
OCF is operating cash flows divided by end-of-year total assets; LEV is leverage calculated by total liabilities divided by total assets; All other variables are defined in Appendix C. All
continuous variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in parentheses are
based on two-tailed tests. Standard errors are clustered by firm.
52
Table 1
Panel A: Sample Selection
Firm-years listed on Shenzhen and Shanghai exchanges 2007-2010*
Firm-year
observations
6,764
Less:Firms in the financial industry
Firms traded on small and medium-sized enterprises(SMEs) board
Firms with missing data required for determinants of internal control strength
Total sample used in multivariate analysis
(120)
(1,401)
(468)
4,775 *Firms traded on the Entrepreneur Section of the Shenzhen Exchange are excluded because the Entrepreneur Section
was not established until 2009 and these firms are not covered by the Internal Control Index constructed by Chen et
al. (2013).
Panel B: Distribution by Year
Year
2007
2008
2009
2010
Total
SOEs
N
787
802
802
805
3,196
Non-SOEs
%
66.41
67.28
66.78
67.25
66.93
53
N
398
390
399
392
1,579
%
33.59
32.72
33.22
32.75
33.07
Total
1,185
1,192
1,201
1,197
4,775
Table 2, Panel A: Descriptive Statistics for Full Sample
Variable
N
Mean
Median
SD
Q1
Q3
ICFR Score
4,775
0.465
0.460
0.108
0.388
0.535
OTHREC
4,775
0.027
0.012
0.042
0.004
0.030
RELATEDLOAN
4,775
0.012
0.000
0.042
0.000
0.003
PRIVATE
2,128
0.027
0.010
0.056
0.004
0.024
EXPOSED
2,796
0.045
0.000
0.207
0.000
0.000
Total Assets
4,775
6,965.7
2,504.1
15,204.4
1,142.3
5,693.1
Sales Revenue
4,775
4,934.8
1,443.7
11,239.0
595.3
3,573.8
# of Segment
4,775
3.486
2.000
2.840
2.000
5.000
ROA
4,775
0.036
0.033
0.080
0.011
0.065
LOSS
4,775
0.122
0.000
0.328
0.000
0.000
MB
4,775
4.342
3.387
5.379
1.980
5.496
EXPORT
4,775
0.414
0.000
0.493
0.000
1.000
M&A
4,775
0.780
1.000
0.414
1.000
1.000
FOREIGNSHR
4,775
0.019
0.000
0.073
0.000
0.000
BIG4
4,775
0.065
0.000
0.246
0.000
0.000
LIST2006
4,775
0.059
0.000
0.236
0.000
0.000
MARKETIZATION
4,775
8.737
8.660
2.043
7.190
11.040
EXCHENGE
4,775
0.377
0.000
0.485
0.000
1.000
TOP1SHR
4,775
0.357
0.337
0.154
0.232
0.476
TOP2to5
4,775
0.104
0.075
0.088
0.034
0.159
INVCOMP
4,775
0.304
0.302
0.163
0.176
0.409
BOARD
4,775
0.512
0.512
0.060
0.490
0.541
CEOCHAIR
4,722
0.132
0.000
0.338
0.000
0.000
PAYRATIO
3,954
3.734
2.763
2.940
1.822
4.592
MGMTSHR
4,773
0.530
0.002
3.258
0.000
0.017
ATO
4,775
0.746
0.625
0.546
0.364
0.963
EISSUE
4,762
0.117
0.000
0.280
0.000
0.009
DISSUE
4,773
0.241
0.107
0.659
-0.048
0.331
Total Assets and Sales Revenues are reported in millions of Chinese Yuan.
54
Table 2, Panel B: Descriptive Statistics for the SOE and Non-SOE Samples
SOEs
Non-SOEs
N=3,196
N=1,579
Test of Differences
Mean
Median
Mean
Median
Mean
Test
Median
Test
ICFR Score
0.475
0.468
0.446
0.441
<0.001
<0.001
OTHREC
0.022
0.011
0.038
0.017
<0.001
<0.001
RELATEDLOAN
0.009
0.000
0.016
0.000
<0.001
<0.001
PRIVATE
0.020
0.009
0.040
0.014
<0.001
<0.001
EXPOSED
0.062
0.000
0.011
0.000
<0.001
<0.001
Total Assets
8,867.4
2,958.6
3,116.4
1,530.6
<0.001
<0.001
Sales Revenue
6,297.7
1,812.6
2,176.0
882.1
<0.001
0.001
# of Segments
3.422
2.000
3.616
2.000
0.027
<0.001
ROA
0.034
0.032
0.039
0.037
0.038
0.008
LOSS
0.114
0.000
0.140
0.000
0.009
0.009
MB
4.120
3.170
4.791
3.807
<0.001
<0.001
EXPORT
0.431
0.000
0.379
0.000
<0.001
<0.001
M&A
0.755
1.000
0.832
1.000
<0.001
<0.001
FOREIGNSHR
0.020
0.000
0.016
0.000
0.105
0.205
BIG4
0.084
0.000
0.027
0.000
<0.001
<0.001
LIST2006
0.068
0.000
0.042
0.000
<0.001
<0.001
MARKETIZATION
8.686
8.660
8.841
8.810
0.014
0.060
EXCHANGE
0.369
0.000
0.392
0.000
0.121
0.121
TOP1SHR
0.384
0.382
0.302
0.262
<0.001
<0.001
TOP2to5
0.100
0.066
0.112
0.093
<0.001
<0.001
INVCOMP
0.308
0.304
0.297
0.294
0.022
0.047
BOARD
0.512
0.511
0.513
0.513
0.969
0.146
CEOCHAIR
0.095
0.000
0.205
0.000
<0.001
<0.001
PAYRATIO
3.542
2.585
4.126
3.223
<0.001
<0.001
MGMTSHR
0.066
0.002
1.469
0.002
<0.001
0.001
ATO
0.784
0.652
0.668
0.537
<0.001
<0.001
EISSUE
0.107
0.000
0.138
0.000
<0.001
0.004
0.006
<0.001
DISSUE
0.260
0.126
0.204
0.067
Total Assets and Sales Revenues are reported in millions of Chinese Yuan.
55
Table 2, Panel C: Mean ICFR Score and Corruption Measures by Industry
RELATED
Industry
N*
ICFR OTHREC
PRIVATE
LOAN
Agriculture, forestry
119
0.427
0.040
0.018
0.022
and fishing
Mining
EXPOSED
0.017
105
0.505
0.013
0.010
0.008
0.219
2,725
0.461
0.023
0.009
0.026
0.033
Utilities
242
0.478
0.014
0.005
0.013
0.095
Construction
104
0.516
0.053
0.007
0.010
0.077
Transportation and
warehousing
197
0.499
0.021
0.010
0.025
0.152
Information
technology
270
0.454
0.045
0.019
0.046
0.037
Distribution and retail
305
0.462
0.034
0.020
0.024
0.023
Real estate
290
0.482
0.029
0.011
0.028
0.041
Service
132
0.482
0.033
0.006
0.026
0.023
Communication and
mass media
41
0.444
0.036
0.010
0.093
0.000
Other Industries
245
0.441
0.048
0.026
0.057
0.029
Manufacturing
Total
4,775 0.465
0.027
0.012
0.027
0.045
*The number of observations varies for each variable due to differences in data availability. N
refers to the number of observations corresponding to ICFR.
Table 2, Panel D: Mean ICFR Score and Corruption Measures by Year
Year
ICFR
OTHREC
RELATEDLOAN
PRIVATE
EXPOSED
2007
0.408
0.035
0.016
0.032
0.047
2008
0.446
0.029
0.013
0.027
0.049
2009
0.489
0.024
0.009
0.027
0.047
2010
0.517
0.022
0.008
0.024
0.036
Total
0.465
0.027
0.012
0.027
0.045
56
Table 2, Panel E: Pearson (Spearman) Correlation
a
a
0.01
-0.07
a
a
a
-0.05
a
-0.04
a
0.05
-0.08
b
0.02
-0.05
a
PRIVATE
-0.11
0.27
0.10
1.00
-0.03
0.08
0.06
-0.31
-0.24
-0.06
-0.03
0.09
-0.08
-0.06
-0.03
EXPOSED
0.05
a
0.00
0.01
-0.07
a
1.00
-0.02
-0.02
0.13
a
0.06
a
0.06
a
-0.01
-0.01
0.02
-0.02
-0.03
ABSDA
-0.05
a
0.09
a
-0.02
1.00
RESTATE
-0.12
a
0.08
b
-0.02
0.04
ASSETS
0.34
a
-0.25
a
0.09
a
-0.10
ATO
0.09
a
-0.08
a
0.06
a
-0.06
BIG4
0.15
a
-0.08
a
.058
a
-0.06
b
0.02
0.06
a
0.02
a
0.06
0.05
a
0.00
-0.39
a
-0.04
a
-0.35
a
0.09
a
-0.10
a
a
0.05
BOARD
0.28
CEOCHAIR
-0.06
a
-0.06
-0.04
a
-0.04
a
-0.12
a
a
0.04
a
-0.07
DISSUE
0.14
EISSUE
0.10
EXCHANGE
0.15
EXPORT
0.06
0.02
0.00
-0.11
0.06
a
-0.06
INVCOM
-0.02
-0.06
LIST2006
0.05
a
-0.08
LOSS
-0.13
a
0.10
M&A
0.01
0.13
a
-0.06
a
0.07
-0.03
0.04
a
-0.02
-0.08
a
a
0.06
a
a
a
0.07
a
a
0.11
0.04
-0.08
a
-0.05
0.10
a
-0.04
b
a
-0.16
a
a
0.12
a
-0.16
a
-0.23
ROA
0.13
SALES
0.32
SEGMENTS
0.09
SOE
0.12
TOP1SHR
0.09
0.05
0.01
0.09
a
-0.09
1.00
0.13
a
0.40
a
0.07
a
-0.14
a
0.18
a
-0.10
a
0.26
a
0.19
a
0.15
a
-0.20
a
0.04
a
0.05
a
0.03
-0.12
a
-0.40
a
0.07
a
0.01
a
0.40
a
0.14
a
0.13
a
-0.03
b
-0.04
a
0.13
a
a
-0.01
-0.01
0.00
a
0.00
-0.03
0.12
a
-0.05
a
0.32
a
a
-0.02
a
-0.04
1.00
0.00
b
0.04
0.01
1.00
-0.02
-0.03
a
1.00
a
0.33
-0.03
b
0.18
a
1.00
-0.05
0.07
0.01
-0.14
a
-0.07
a
-0.05
a
0.01
a
a
0.08
a
0.07
a
0.07
-0.05
a
0.05
a
-0.01
-0.01
-0.03
a
0.07
-0.02
0.03
-0.04
0.34
a
0.00
0.23
b
0.00
a
-0.04
b
a
a
-0.09
-0.03
a
0.23
a
a
a
0.04
a
0.12
b
a
-0.04
0.02
a
007
a
0.06
0.04
a
0.36
a
-0.06
0.02
0.14
a
-0.24
a
0.13
a
-0.06
a
-0.03
b
0.00
0.07
a
0.01
0.00
-0.05
a
b
0.00
a
a
0.06
0.14
a
0.07
a
-0.14
a
-0.04
0.00
-0.05
0.10
b
0.07
a
-0.04
0.08
a
0.02
-0.29
b
-0.02
-0.03
0.02
a
0.11
0.07
a
-0.06
a
0.00
-0.03
0.20
a
0.12
a
a
0.05
0.03
a
0.06
a
-0.17
a
-0.03
b
-0.24
0.02
a
0.12
a
0.10
a
b
-0.08
a
-0.08
0.03
a
0.20
a
0.85
0.57
-0.02
0.12
a
-0.01
0.27
a
-0.05
a
0.30
-0.04
-0.12
a
-0.04
1.00
0.15
a
0.18
a
0.00
a
1.00
0.07
a
-0.07
a
-0.05
a
0.05
a
0.06
a
-0.19
a
0.05
a
0.18
a
0.07
a
1.00
-0.04
a
0.04
a
-0.08
a
-0.12
a
0.04
a
-0.02
0.01
-0.07
a
-0.04
a
-0.01
0.00
0.03
b
0.06
a
0.01
-0.02
-0.03
a
-0.01
0.02
-0.01
a
a
0.10
a
0.05
a
0.04
b
b
-0.10
a
-0.04
a
0.07
0.09
0.30
0.14
0.12
a
0.13
-0.03
-0.12
0.09
0.03
0.00
0.01
-0.03
a
a
0.05
a
0.04
a
0.06
a
0.07
a
0.08
-0.03
0.04
0.05
a
0.03
0.09
a
0.10
a
0.01
-0.02
0.15
a
0.05
a
-0.14
a
0.25
a
0.03
a
0.11
a
0.15
a
b
a
0.02
a
0.21
a
0.18
b
-0.01
-0.03
0.03
a
-0.02
-0.15
a
0.09
a
-0.02
-0.13
a
0.15
a
-0.03
a
-0.07
b
0.08
a
a
0.05
0.03
b
0.02
0.08
a
0.06
a
-0.04
a
-0.15
a
-0.03
-0.15
a
-0.13
b
-0.02
0.01
0.01
a
0.05
a
0.11
a
0.14
0.00
a
0.04
a
0.19
a
-0.04
0.01
0.06
a
0.08
a
0.17
a
0.13
a
0.04
a
-0.05
a
0.10
a
-0.02
-0.10
a
0.04
b
0.08
b
0.07
a
0.11
a
-0.12
a
-0.09
b
0.02
0.09
0.16
a
0.00
0.01
a
b
0.03
-0.02
a
a
0.00
0.04
0.13
b
0.00
-0.02
-0.05
0.05
a
-0.01
-0.09
0.00
a
0.00
0.22
a
0.03
a
0.03
a
0.05
b
0.05
b
0.03
a
0.03
a
0.02
0.05
a
0.08
a
0.01
0.23
0.10
a
0.13
a
0.16
-0.04
a
-0.01
0.01
-0.02
0.12
b
0.00
0.05
a
0.13
-0.04
a
1.00
-0.01
-0.05
0.01
-0.02
-0.08
a
-0.65
a
-0.22
-0.02
-0.04
-0.01
-0.01
-0.01
1.00
0.03
0.02
0.01
0.05
a
0.00
-0.03
0.02
-0.09
b
0.03
0.09
a
-.001
0.03
1.00
a
a
a
0.10
0.00
0.01
-0.04
b
-0.02
0.03
a
-0.14
a
-0.03
-0.06
a
a
0.04
b
0.00
0.02
-0.20
a
0.04
a
0.21
a
0.12
a
-0.09
b
0.03
-0.08
-0.05
a
0.09
a
0.00
-0.09
a
a
0.06
-0.57
a
0.10
a
-0.21
0.17
a
b
0.02
0.18
-0.10
-0.01
-0.02
a
a
0.05
0.00
0.00
0.02
0.05
0.07
b
0.14
-005
0.19
0.03
0.03
0.11
a
b
a
0.01
-0.10
a
0.11
a
0.00
-0.02
-0.01
0.10
b
0.09
0.21
a
3.00
a
a
0.04
a
-0.04
-0.10
b
0.04
0.04
a
0.02
0.03
a
0.06
a
0.07
a
a
0.05
0.03
b
0.00
0.17
a
1.00
0.14
a
a
0.15
a
0.06
a
0.19
1.00
0.20
-0.01
-0.03
a
-0.21
a
0.11
a
0.22
a
0.24
a
1.00
0.18
a
0.30
a
0.01
a
1.00
a
-0.04
a
0.27
a
-0.05
1.00
0.25
a
0.15
a
0.29
a
0.01
0.27
a
1.00
0.07
-0.02
0.12
a
0.11
a
-0.12
a
-0.09
a
-0.06
-0.01
0.03
a
a
a
-0.09
a
1.00
a
a
a
0.05
0.04
-0.02
a
0.07
a
-0.04
a
a
0.09
0.16
a
a
-0.03
a
0.01
a
0.00
b
0.05
a
0.09
a
a
a
a
0.05
a
0.12
a
-0.13
a
-0.09
0.00
-0.04
a
-0.03
-0.10
-0.05
a
0.03
b
-0.01
-0.23
a
0.16
-0.04
a
1.00
-0.07
b
b
a
-0.05
b
b
b
a
a
0.29
-0.01
-0.08
b
a
0.05
a
a
-0.04
a
b
a
a
a
a
b
a
0.00
0.33
a
b
a
0.54
a
a
-0.02
0.03
a
0.07
a
-0.04
0.09
a
0.17
a
0.06
a
a
a
0.11
0.04
0.02
0.34
0.08
a
0.11
a
0.03
-0.10
a
0.12
-0.04
-0.07
0.02
a
b
a
0.07
-0.05
-0.07
a
0.05
-0.04
-0.01
0.86
0.05
-0.19
-0.12
-0.02
a
a
a
-0.05
a
0.17
0.05
-0.01
a
-0.14
a
a
a
a
0.07
-0.05
0.11
0.21
-0.01
a
a
-0.02
0.01
0.13
-0.18
0.12
a
a
-0.05
-0.17
0.02
-0.18
0.01
1.00
-0.02
a
a
a
-0.13
-0.43
-0.09
0.01
b
a
a
a
-0.02
1.00
0.13
a
a
0.01
b
-0.13
0.02
-0.11
-0.06
-0.05
0.01
-0.09
a
-0.20
a
b
a
-0.07
a
a
0.03
0.06
b
-0.08
a
0.09
a
-0.03
-0.16
-0.06
0.00
-0.18
a
0.13
0.00
-0.01
a
-0.18
a
-0.30
a
a
0.00
-0.03
a
0.01
b
0.04
a
0.09
a
0.3
-0.02
0.04
a
0.35
a
0.02
a
a
a
0.11
a
-0.03
0.07
b
a
a
a
a
a
a
0.07
0.00
a
a
a
0.17
a
0.06
0.09
0.20
b
-0.01
a
a
a
a
b
0.14
a
a
a
a
a
0.02
a
a
-0.12
-0.04
-0.05
0.04
a
0.03
-0.03
-0.05
-0.02
-0.14
0.01
a
0.02
0.01
1.00
-0.22
0.10
0.04
a
0.02
a
a
a
a
1.00
-0.13
a
a
0.04
0.09
-0.48
0.22
a
a
-0.07
a
a
0.10
-0.01
0.10
-0.03
0.00
-0.02
-0.06
0.00
-0.02
b
0.14
a
a
a
-0.03
0.08
-0.23
0.04
-0.05
-0.05
a
0.01
a
a
0.01
a
-0.02
a
0.06
a
-0.09
-0.04
a
a
a
0.10
0.00
0.10
-0.10
-0.03
a
0.04
0.18
0.03
0.05
-0.02
0.04
a
0.20
0.01
0.11
-0.03
0.00
-0.05
a
0.00
0.00
a
PAYRATIO
-0.03
0.00
-0.05
0.08
0.13
-0.03
0.03
0.03
-0.02
-0.03
0.00
a
0.00
0.03
0.04
-0.06
0.14
a
-0.12
0.21
-0.04
-0.04
-0.07
b
a
-0.02
a
a
MGMTSHR
b
b
0.01
a
b
-0.09
0.06
0.04
0.06
0.01
a
-0.08
-0.02
-0.02
b
0.00
a
-0.14
-0.02
0.04
-0.03
a
a
MB
a
009
0.03
-0.10
0.04
0.05
-0.06
a
0.09
a
0.01
a
0.03
b
a
-0.01
-0.05
-0.02
a
0.11
0.10
-0.01
-0.05
a
b
0.06
a
a
b
b
-0.07
-0.05
0.00
a
a
a
-0.13
b
-0.06
a
-0.03
-0.01
a
-0.06
-0.03
a
0.06
-0.07
a
-0.04
0.12
a
a
0.11
-0.08
0.01
a
-0.05
a
a
a
-0.09
b
-0.05
-0.14
a
-0.08
-0.01
a
-0.07
-0.01
b
a
0.06
0.02
a
b
-0.06
a
1.00
a
a
a
-0.03
a
a
a
a
FOREIGHSHR
MARKET
0.06
a
b
a
a
0.08
TOP1SHR
a
-0.08
b
-0.04
-0.01
a
SOE
a
0.05
-0.04
-0.09
a
a
0.06
a
SEGMENTS
a
-0.05
a
0.13
a
SALES
a
-0.12
a
a
0.06
a
ROA
-0.17
a
0.06
PAYRATIO
a
a
a
MGMTSHR
0.04
0.14
a
MB
a
0.19
1.00
-0.06
a
MARKET
0.10
0.37
a
0.23
a
M&A
-0.02
a
a
0.16
a
LOSS
-0.27
0.36
-0.05
a
0.08
a
LIST2006
0.06
a
-0.12
a
0.37
INVCOM
0.13
a
FOREIGHSHR
-0.12
EXPORT
b
a
EXCHANGE
-0.04
0.00
EISSUE
0.06
DISSUE
a
CEOCHAIR
-0.14
BOARD
a
a
RELATEDLOAN
BIG4
-0.12
1.00
a
a
ATO
-0.15
a
1.00
OTHREC
a
ASSETS
RESTATE
ABSDA
EXPOSED
a
PRIVATE
RELATEDLOAN
OTHREC
ICFR
ICFR
b
a
0.01
a
-0.11
a
-0.10
b
a
0.14
a
0.32
-0.05
a
a
a
a
a
0.01
a
All variables are described in Appendix. Pearson correlations are reported above the diagonal, and Spearman correlations are reported in the off diagonal. All continuous variables are winsorized at 1% and 99% to
mitigate outliers. a, b, indicate statistical significance at the 0.01, 0.05 level, respectively.
57
Table 3: Internal Control and Tunneling
Panel A: Full Sample
Intercept
Predicted
Signs
?
ICFR
−
ASSETS
−
ROA
−
TOP1SHR
−
INVCOMP
−
BOARD
−
CEOCHAIR
+
MARKETIZATION
−
BIG4
−
EXCHANGE
?
Year effects
Industry effects
Dep. Var.=
OTHREC
0.183***
(0.000)
-0.013**
(0.048)
-0.006***
(0.000)
-0.045***
(0.001)
-0.027***
(0.000)
-0.012**
(0.012)
0.011
(0.836)
0.001
(0.373)
-0.001***
(0.005)
0.006
(0.994)
0.002
(0.276)
Included
Included
Dep. Var.= RELATEDLOAN
0.132***
(0.000)
-0.021***
(0.010)
-0.005***
(0.000)
-0.036**
(0.015)
-0.012***
(0.009)
-0.002
(0.358)
-0.004
(0.349)
-0.003
(0.574)
0.001
(0.893)
0.013
(1.000)
0.001
(0.515)
Included
Included
4,722
0.139
4,722
0.053
No. of Obs.
Adjusted R2
All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate
outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in
parentheses are based on one-tailed tests for variables with predicted signs and two-tailed tests for variables
without predicted signs. Standard errors are clustered by firm.
58
Table 3: Internal Control and Tunneling (continued)
Panel B: SOEs versus Non-SOEs
Dep. Var.
OTHREC
Predicted
SOEs
Non-SOEs
Signs
?
0.144***
0.229***
Intercept
(0.000)
(0.000)
−
-0.006
-0.032**
ICFR
(0.207)
(0.025)
−
-0.004*** -0.008***
ASSETS
(0.000)
(0.000)
−
-0.074***
-0.021
ROA
(0.000)
(0.178)
−
-0.021***
-0.025**
TOP1SHR
(0.001)
(0.014)
−
-0.006
-0.026***
INVCOMP
(0.156)
(0.007)
−
-0.006
0.060
BOARD
(0.250)
(0.929)
+
0.004
-0.003
CEOCHAIR
(0.129)
(0.779)
−
-0.0002
-0.003***
MARKETIZATION
(0.307)
(0.001)
−
0.002
-0.004
BIG4
(0.845)
(0.165)
?
-0.001
0.007*
EXCHANGE
(0.744)
(0.076)
Included
Included
Year effects
Included
Included
Industry effects
No. of Obs.
Adjusted R2
3,154
0.131
1,568
0.148
RELATEDLOAN
SOEs
Non-SOEs
0.089***
(0.000)
-0.006
(0.240)
-0.003***
(0.000)
-0.066***
(0.001)
-0.009**
(0.037)
-0.002
(0.331)
-0.014*
(0.086)
0.001
(0.187)
0.001
(0.962)
0.010
(0.998)
0.003*
(0.080)
Included
Included
0.204***
(0.001)
-0.049***
(0.006)
-0.008***
(0.000)
-0.0004
(0.494)
-0.016*
(0.068)
0.00002
(0.501)
0.031
(0.753)
-0.006
(0.528)
0.0004
(0.665)
0.008
(0.936)
-0.003
(0.519)
Included
Included
3,154
0.049
1,568
0.070
Test of differences between coefficients for OTHREC
SOEs vs. non-SOEs
Coefficient Diff.
Z-statistic
ICFR
0.025*
1.409
p-value
0.068
Test of differences between coefficients for RELATEDLOAN
SOEs vs. non-SOEs
Coefficient Diff.
Z-statistic
0.043**
2.035
ICFR
p-value
0.021
All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate
outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in
parentheses are based on one-tailed tests for variables with predicted signs and two-tailed tests for variables
without predicted signs. Standard errors are clustered by firm.
59
Table 4: Internal Control and Private Consumption
Dependent variable= PRIVATE
Predicted
Signs
Intercept
?
ICFR
−
PAYRATIO
−
MGMTSHR
−
ATO
−
ROA
?
LOSS
?
EISSUE
+
DISSUE
+
BIG4
−
TOP1SHR
−
INVCOMP
−
BOARD
−
CEOCHAIR
+
MARKETIZATION
−
Year effects
Industry effects
No. of Obs.
Adjusted R2
Full Sample
SOEs
Non-SOEs
0.071***
(0.006)
-0.025**
(0.047)
-0.001**
(0.015)
0.001
(0.810)
-0.018***
(0.000)
0.0003
(0.990)
0.019***
(0.003)
-0.002
(0.731)
-0.001
(0.707)
-0.002
(0.245)
-0.026***
(0.004)
-0.005
(0.268)
-0.026
(0.223)
0.006
(0.136)
-0.0004
(0.352)
Included
Included
0.027
(0.122)
-0.002
(0.459)
-0.000
(0.354)
-0.002**
(0.010)
-0.015***
(0.000)
-0.010
(0.724)
0.009
(0.168)
-0.0003
(0.546)
-0.001
(0.663)
-0.008***
(0.004)
-0.012
(0.104)
-0.010
(0.133)
0.013
(0.708)
0.003
(0.301)
0.001
(0.878)
Included
Included
0.195***
(0.004)
-0.069*
(0.027)
-0.003***
(0.004)
0.001
(0.837)
-0.026***
(0.000)
0.052
(0.282)
0.034***
(0.009)
-0.002
(0.611)
0.0001
(0.493)
0.009
(0.731)
-0.057**
(0.011)
-0.001
(0.485)
-0.124*
(0.082)
0.009
(0.273)
-0.004*
(0.098)
Included
Included
1,796
0.111
1,235
0.068
561
0.162
Test of coefficient difference
ICFR
Coefficient Diff.
0.068**
SOE vs. Non-SOE
Z-statistic
1.744
p-value
0.040
All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to
mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. Pvalues reported in parentheses are based on one-tailed tests for variables with predicted signs and twotailed tests for variables without predicted signs. Standard errors are clustered by firm.
60
Table 5: Internal Control and Exposed Corruption Cases
Dependent variable=EXPOSED
Predicted
Signs
Intercept
?
ICFR
−
PAYRATIO
−
MGMTSHR
−
SALE
+
ROA
−
EISSUE
+
DISSUE
+
TOP1SHR
−
INVCOMP
−
BOARD
−
CEOCHAIR
+
MARKETIZATION
−
Year effects
Industry effects
No. of Obs.
Pseudo R2
Full
SOE
Non-SOE
-9.075***
(0.001)
0.701
(0.747)
-0.132**
(0.012)
-0.142*
(0.079)
0.293***
(0.003)
-2.179
(0.150)
-0.668
(0.975)
0.107
(0.206)
1.115
(0.854)
-0.255
(0.364)
1.122
(0.665)
0.349
(0.184)
-0.069
(0.179)
Included
Included
-8.941***
(0.001)
0.509
(0.671)
-0.101**
(0.034)
-0.115
(0.377)
0.329***
(0.001)
-2.716
(0.131)
-0.659
(0.967)
0.116
(0.207)
1.803
(0.936)
-0.738
(0.182)
0.571
(0.587)
0.086
(0.420)
-0.104
(0.103)
Included
Included
3.219
(0.718)
-5.032*
(0.083)
-0.349***
(0.004)
-0.648***
(0.004)
-0.988
(0.984)
7.602
(0.993)
1.182
(0.116)
0.132
(0.417)
-8.383**
(0.045)
3.302
(0.862)
1.803
(0.658)
1.262**
(0.019)
0.411
(0.940)
Included
Included
2,389
0.105
1,723
0.107
666
0.385
Test of coefficient difference
SOEs vs. Non-SOEs
Coefficient Diff.
Z-statistic
p-value
ICFR
5.541*
1.589
0.056
All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to
mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 level,
respectively. P-values reported in parentheses are based on one-tailed tests for variables with predicted
signs and two-tailed tests for variables without predicted signs. Standard errors are clustered by firm.
61
Table 6: Corporate Corruption and Future Operating Performance
Dependent variable= Future Operating Performance (OIt+1)
Predicted
Column
Column
Signs
A
B
0.004
-0.001
Intercept
(0.848)
(0.945)
–
-0.106***
OTHREC
(0.001)
–
-0.085**
RELATEDLOAN
(0.011)
–
PRIVATE
EXPOSED
–
PPE
?
GROWTH
?
SEGMENTS
?
EXPORT
?
AGE
?
BIG4
?
OI
?
ASSETS
?
TOP1SHR
?
Year effects
Industry effects
No. of Obs.
Adjusted R2
Column
C
-0.022
(0.517)
Column
D
0.014
(0.517)
-0.001
(0.483)
-0.002**
(0.019)
-0.002*
(0.056)
0.002
(0.428)
-0.003
(0.144)
-0.003
(0.320)
0.008**
(0.043)
0.583***
(0.000)
0.002*
(0.076)
0.016**
(0.022)
Included
Included
-0.002**
(0.044)
-0.002*
(0.050)
0.001
(0.471)
-0.003
(0.158)
-0.003
(0.297)
0.009**
(0.026)
0.589***
(0.000)
0.002
(0.112)
0.018***
(0.009)
Included
Included
-0.003*
(0.092)
-0.004*
(0.099)
0.005*
(0.088)
-0.008**
(0.020)
-0.011***
(0.009)
0.002
(0.689)
0.608***
(0.000)
0.004
(0.111)
0.015
(0.171)
Included
Included
-0.008**
(0.029)
-0.004***
(0.002)
-0.003
(0.158)
0.0002
(0.920)
-0.003
(0.182)
0.002
(0.434)
0.008*
(0.056)
0.597***
(0.000)
0.002
(0.175)
0.004
(0.606)
Included
Included
4,767
0.406
4,767
0.406
2,124
0.410
2,795
0.433
PPE is the natural logarithm of gross property, plant and equipment at the end of year t. GROWTH is
annual percentage sales growth in year t. AGE is the natural logarithm of the number of years that a
company is publicly traded. All other variables are defined in Appendix C. All continuous variables are
winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01,
0.05, 0.10 level, respectively. P-values reported in parentheses are based on one-tailed tests for
variables with predicted signs and two-tailed tests for variables without predicted signs. Standard errors
are clustered by firm.
62
Table 7: Ownership Structure and the Effectiveness of Internal Control
Panel A: Full Sample
Predicted sign
OTHREC
RELATEDLOAN
PRIVATE
EXPOSED
ICFR
−
0.003
-0.005
-0.008
-0.273
(0.608)
(0.322)
(0.341)
(0.413)
ICFR×TOP2to5IND
−
-0.030**
-0.030**
-0.034
1.830
(0.016)
(0.025)
(0.145)
(0.851)
TOP2to5IND
−
0.013
0.015
0.016
-0.584
(0.972)
(0.975)
(0.842)
(0.255)
Control variables
Included
Included
Included
Included
# Obs.
4722
4,722
1,796
2.389
0.140
0.054
0.049
0.109
Adjusted (Pseudo)R2
Panel B: SOEs versus Non-SOEs
OTHREC
RELATEDLOAN
PRIVATE
SOEs
Non-SOEs
SOEs
Non-SOEs
SOEs
Non-SOEs
ICFR
-0.005
0.013
-0.000
-0.010
-0.007
-0.015
(0.309)
(0.753)
(0.491)
(0.337)
(0.376)
(0.375)
ICFR×TOP2to5IND
-0.003
-0.076***
-0.012
-0.065**
0.011
-0.088*
(0.427)
(0.003)
(0.202)
(0.023)
(0.342)
(0.098)
TOP2to5IND
0.001
0.032
0.008
0.027
-0.006
0.045
(0.547)
(0.993)
(0.868)
(0.945)
(0.330)
(0.899)
Control variables
Included
Included
Included
Included
Included
Included
# Obs.
3,154
1,568
3,154
1,568
1,235
561
0.131
0.153
0.049
0.073
0.040
0.062
Adjusted (Pseudo)R2
EXPOSED
SOEs
Non-SOEs
-0.483
-0.590
(0.358)
(0.445)
1.952
-6.154*
(0.838)
(0.096)
-0.593
2.201
(0.275)
(0.832)
Included
Included
1,723
666
0.112
0.394
Test of coefficient difference
ICFR×TOP2to5IND
OTHREC
SOEs vs. Non-SOEs
Coefficient Diff.
p-value
0.073***
0.008
RELATEDLOAN
SOEs vs. Non-SOEs
Coefficient Diff.
p-value
0.053*
0.066
63
PRIVATE
SOEs vs. Non-SOEs
Coefficient Diff.
p-value
0.099*
0.088
EXPOSED
SOEs vs. Non-SOEs
Coefficient Diff.
p-value
8.106*
0.056
Table 8: Internal Control and CEO Promotions
Dependent variable=PROMOTION
Predicted
Full
Signs
ICFR
+
SALES
?
OCF
?
ROA
+
LEV
?
MB
?
CEOAGE
?
EDUCATION
+
TENURE
+
TOP1SHR
?
MARKETIZATION
+
Constant1
?
Constant 2
?
Constant
?
Year effects
Industry effects
No. of Obs.
Pseudo R2
Alla
Turnover Onlyb
NonSOEs
Sample
SOEs
0.915**
1.019**
0.752
(0.016)
(0.026)
(0.155)
0.090*** 0.082** 0.139***
(0.003)
(0.033)
(0.009)
0.230
-0.494
1.566**
(0.608)
(0.412)
(0.022)
0.762
-0.206
1.214*
(0.115)
(0.885)
(0.092)
-0.306** -0.713*** -0.127
(0.015)
(0.000)
(0.464)
-0.004
0.017
-0.025**
(0.597)
(0.113)
(0.039)
-0.261** -0.476***
0.265
(0.039)
(0.002)
(0.263)
0.025
0.021
0.034
(0.309)
(0.375)
(0.327)
0.011
0.027
-0.022
(0.274)
(0.122)
(0.756)
0.053
0.258
-0.398
(0.846)
(0.421)
(0.475)
0.007
0.023
-0.029
(0.358)
(0.182)
(0.790)
0.440
0.345
0.995
(0.497)
(0.686)
(0.391)
5.366*** 5.324*** 5.979***
(0.000)
(0.000)
(0.000)
Included
Included
Included
Included
Included
Included
4,573
0.012
3,071
0.017
1,502
0.027
Full
Sample
1.656**
(0.025)
0.136**
(0.030)
0.332
(0.705)
-0.309
(0.608)
-0.432
(0.174)
-0.001
(0.939)
-0.461*
(0.076)
0.082
(0.208)
0.033
(0.204)
0.081
(0.886)
0.015
(0.357)
SOEs
1.552*
(0.071)
0.133
(0.108)
-1.635
(0.170)
-0.087
(0.523)
-1.022**
(0.033)
0.032*
(0.089)
-0.783**
(0.011)
0.088
(0.262)
0.076*
(0.066)
0.435
(0.529)
0.035
(0.249)
NonSOEs
1.448
(0.177)
0.232**
(0.045)
2.918*
(0.064)
0.151
(0.465)
-0.301
(0.450)
-0.034
(0.173)
0.352
(0.471)
-0.051
(0.618)
-0.091
(0.866)
-0.645
(0.572)
-0.097
(0.920)
-4.549*** -4.793*** -4.635*
(0.000)
(0.003)
(0.056)
Included
Included Included
Included
Included Included
772
0.040
506
0.063
266
0.083
a
: Includes all firm-years; the dependent variable equals 1 if the CEO is promoted in the following year, 0 if there is no
CEO turnover, and –1 if the CEO is demoted in the following year.
b
: Includes only firm-years of CEO turnover; the dependent variable equals 1 if the CEO gets promoted in the
following year, and 0 otherwise.
We collect the CEO turnover data from 2008-2011 and eliminate cases where the CEO left office due to retirement or
health reason, and cases where we cannot identify her next appointment. We consider the CEO as have been promoted
if in the following year she left the CEO position and became (1) the chairman of the board at the same company (2)
the CEO or the chairman of the board in the parent company, (3) the CEO at a larger public company, where the assets
of the new company are at least 20% larger than the former company, or (4) a high-ranking government official. OCF
is the operating cash flows divided by end-of-year total assets. LEV is leverage calculated by total liabilities divided by
total assets. EDUCATION is a categorical variable measuring the highest degree of education the CEO holds;
EDUCATION equals 4 if the CEO’s highest degree is a doctorate, 3 if it is a master’s degree, 2 if it is a college
degrees, 1 if it is a 3-year college or other. TENURE measures the number of years that the CEO has assumed the
position. CEOAGE is an indicator variable that equals to one if the CEO is 55 years old or older, and zero otherwise.
All other variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate
outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in
parentheses are based on two-tailed tests. Standard errors are clustered by firm.
64
Table 9: Internal Control and Corporate Corruption: 2SLS
Panel A: Full Sample
Predicted sign
OTHREC
෣
−
-0.103**
‫ܴܨܥܫ‬
(0.029)
Included
Control variables
4722
# Obs.
0.140
Adjusted (Pseudo)R2
RELATEDLOAN
-0.139***
(0.007)
Included
4,722
0.053
PRIVATE
-0.272***
(0.000)
Included
1,796
0.134
EXPOSED
1.423
(0.615)
Included
2.389
0.106
Panel B: SOEs versus Non-SOEs
OTHREC
SOEs
0.050
(0.847)
Included
3,154
0.131
෣
‫ܴܨܥܫ‬
Control variables
# Obs.
Adjusted (Pseudo)R2
RELATEDLOAN
Non-SOEs
-0.226**
(0.031)
Included
1,568
0.148
SOEs
-0.031
(0.229)
Included
3,154
0.048
Non-SOEs
-0.348***
(0.008)
Included
1,568
0.069
PRIVATE
SOEs
-0.192***
(0.000)
Included
1,235
0.087
EXPOSED
Non-SOEs
-0.426***
(0.002)
Included
561
0.187
SOEs
1.020
(0.577)
Included
1,723
0.110
Non-SOEs
-46.335**
(0.013)
Included
666
0.438
Test of coefficient difference
OTHREC
SOEs vs. Non-SOEs
෣
‫ܴܨܥܫ‬
Coefficient Diff.
0.275**
p-value
0.017
RELATEDLOAN
SOEs vs. Non-SOEs
Coefficient Diff.
0.318**
p-value
0.017
PRIVATE
SOEs vs. Non-SOEs
Coefficient Diff.
0.234*
p-value
0.061
EXPOSED
SOEs vs. Non-SOEs
Coefficient Diff.
47.354**
p-value
0.014
෣ , is the predicted value of ICFR from the first-stage regression (see Appendix D). All other variables are defined in Appendix C. All continuous
The variable of interest, ‫ܴܨܥܫ‬
variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively, under one-tailed tests for
variables with predicted signs and two-tailed tests for variables without predicted signs. Standard errors are clustered by firm.
65